From ad128707a8472e81a88ad6556699639ba7351e78 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 7 Aug 2019 16:59:40 +1000
Subject: [PATCH 01/82] add space

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 4ea9305e2..0f8c4fe20 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,3 +14,4 @@ pytest==3.9.3
 pytest-benchmark>=3.1
 pytest-cov>=2.6.0
 coveralls>=1.5.1
+ 

From ace4fec29121c894cddf167e76b45885ac8994da Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 7 Aug 2019 18:06:49 +1000
Subject: [PATCH 02/82] Add the definition of the attri2vecLinkGenerator, which
 helps generate a batch of (feature of target node, index of context node,
 label of node pair) pairs per iteration, for training the attri2vec model

---
 stellargraph/mapper/link_mappers.py | 125 ++++++++++++++++++++++++++--
 1 file changed, 119 insertions(+), 6 deletions(-)
 mode change 100644 => 100755 stellargraph/mapper/link_mappers.py

diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
old mode 100644
new mode 100755
index 06f19b3fd..5b086cda3
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -24,6 +24,7 @@
     "OnDemandLinkSequence",
     "GraphSAGELinkGenerator",
     "HinSAGELinkGenerator",
+    "attri2vecLinkGenerator"
 ]
 
 import random
@@ -178,7 +179,7 @@ class OnDemandLinkSequence(Sequence):
     :class:`GraphSAGELinkGenerator` ` .
 
     Args:
-        generator: An instance of :class:`GraphSAGELinkGenerator`.
+        generator: An instance of :class:`GraphSAGELinkGenerator` or 'attri2vecLink Generator'.
         sampler:  An instance of :class:`UnsupervisedSampler` that encapsulates the neighbourhood sampling of a graph.
         The generator method of this class returns `batch_size` of positive and negative samples on demand.
     """
@@ -205,11 +206,18 @@ def __init__(self, generator, walker):
                 * self.walker.number_of_walks
             )  # an estimate of the  upper bound on how many samples are generated in each epoch
 
-            print(
-                "Running GraphSAGELinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
-                    round(self.data_size / self.generator.batch_size)
-                )
-            )
+            if isinstance(self.generator, GraphSAGELinkGenerator):
+                print(
+                        "Running GraphSAGELinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
+                                round(self.data_size / self.generator.batch_size)
+                       )
+                     )
+            else:
+                print(
+                        "Running attri2vecLinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
+                                round(self.data_size / self.generator.batch_size)
+                       )
+                     )
 
             self._gen = self.walker.generator(
                 self.generator.batch_size
@@ -607,3 +615,108 @@ def flow(self, link_ids, targets=None, shuffle=False):
             )
 
         return LinkSequence(self, link_ids, targets, shuffle)
+
+class attri2vecLinkGenerator:
+    """
+    A data generator for link prediction with Homogeneous attri2vec models
+
+    At minimum, supply the StellarGraph the batch size.
+
+    The supplied graph should be a StellarGraph object that is ready for
+    machine learning. Currently the model requires node features for all
+    nodes in the graph.
+
+    Use the :meth:`.flow` method supplying the nodes and (optionally) targets,
+    or an UnsupervisedSampler instance that generates node samples on demand,
+    to get an object that can be used as a Keras data generator.
+
+    Example::
+
+        G_generator = attri2vecLinkGenerator(G, 50)
+        train_data_gen = G_generator.flow(edge_ids)
+
+    Args:
+        G (StellarGraph): A machine-learning ready graph.
+        batch_size (int): Size of batch of links to return.
+        num_samples: for compatibility with GraphSAGE
+        seed (int or str), optional: Random seed for the sampling methods.
+        name, optional: Name of generator
+    """
+
+    def __init__(self, G, batch_size, num_samples=[1,1], seed=None, name=None):
+        if not isinstance(G, StellarGraphBase):
+            raise TypeError("Graph must be a StellarGraph object.")
+
+        G.check_graph_for_ml(features=True)
+
+        self.graph = G
+        self.num_samples = num_samples
+        self.batch_size = batch_size
+        self.name = name
+
+        # We need a schema for compatibility with HinSAGE
+        self.schema = G.create_graph_schema(create_type_maps=True)
+
+        # The sampler used to generate random samples of neighbours
+        #self.sampler = SampledBreadthFirstWalk(G, graph_schema=self.schema, seed=seed)
+
+    def sample_features(self, head_links, sampling_schema):
+        """
+        Sample content features of the target nodes and the ids of the context nodes
+        and return these as a list of feature arrays for the attri2vec algorithm.
+
+        Args:
+            head_links: An iterable of edges to perform sampling for.
+            sampling_schema: The sampling schema for the model, for compatibility
+            with GraphSAGE and HinSAGE
+
+        Returns:
+            a list of feaure arrys, with each element being the feature of a
+            target node and the id of the corresponding context node
+        """
+
+        target_ids = [head_link[0] for head_link in head_links]
+        context_ids = [head_link[1] for head_link in head_links]
+        target_feats = self.graph.get_feature_for_nodes(target_ids)
+        batch_feats = [target_feats, np.array(context_ids)]
+
+        return batch_feats
+
+    def flow(self, link_ids, targets=None, shuffle=False):
+        """
+        Creates a generator/sequence object for training or evaluation
+        with the supplied edge IDs and numeric targets.
+
+        The edge IDs are the edges to train or inference on. They are
+        expected to by tuples of (source_id, destination_id).
+
+        The targets are an array of numeric targets corresponding to the
+        supplied link_ids to be used by the downstream task. They should
+        be given in the same order as the list of link IDs.
+        If they are not specified (for example, for use in prediction),
+        the targets will not be available to the downsteam task.
+
+        Note that the shuffle argument should be True for training and
+        False for prediction.
+
+        Args:
+            link_ids (list or UnsupervisedSampler): an iterable of (src_id, dst_id) tuples
+                specifying the edges or an UnsupervisedSampler object that has a generator
+                method to generate samples on the fly.
+            targets (optional, array): a 2D array of numeric targets with shape
+                `(len(link_ids), target_size)`
+            shuffle (optional, bool): If True the node_ids will be shuffled at each
+                epoch, if False the node_ids will be processed in order.
+
+        Returns:
+            An OnDemandLinkSequence object to use with the attri2vec model.
+        """
+        # Pass sampler to on-demand link sequence generation
+        if isinstance(link_ids, UnsupervisedSampler):
+            return OnDemandLinkSequence(self, link_ids)
+
+        else:
+            raise TypeError(
+                "Argument to .flow not recognised. "
+                "Please pass a list of samples or a UnsupervisedSampler object."
+            )

From 8df75763855a7459beb266bb32ed09ada40b636c Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 7 Aug 2019 18:10:24 +1000
Subject: [PATCH 03/82] Add the implementation of attri2vec, and investigate
 its performance on out-of-sample node link prediction.

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 769 ++++++++++++++++++
 1 file changed, 769 insertions(+)
 create mode 100644 demos/embeddings/stellargraph-attri2vec-DBLP.ipynb

diff --git a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
new file mode 100644
index 000000000..8abfad166
--- /dev/null
+++ b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
@@ -0,0 +1,769 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Infer Representations for Out-of-sample Nodes Through attri2vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
+    "\n",
+    "## Dataset\n",
+    "This demo uses a DBLP citation network, a subgraph extracted from [DBLP-Citation-network V3](https://aminer.org/citation). To form this subgraph, papers from four subjects are extracted according to their venue information: *Database, Data Mining, Artificial Intelligence and Computer Vision*, and papers with no citations are removed. The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word. By ignoring the citation direction, we take the DBLP subgraph as an undirected network.\n",
+    "\n",
+    "As papers in DBLP are attached with publication year, the DBLP network with the dynamic property can be used to study the problem of out-of-sample node representation learning. From the DBLP network, we construct four in-sample subgraphs using papers published before 2006, 2007, 2008 and 2009, and denote the four subgraphs as DBLP2006, DBLP2007, DBLP2008, and DBLP2009. For each subgraph, the remaining papers are taken as out-of-sample nodes. We consider the case where new coming nodes have no links. We predict the links of out-of-sample nodes using the learned out-of-sample node representations and compare its performance with the node content feature baseline.\n",
+    "\n",
+    "\n",
+    "\n",
+    "## attri2vec\n",
+    "\n",
+    "For networks attached with node content attributes, attri2vec infers node representations by discovering a latent node attribute subspace that respects network structure in a more consistent way. To transform network nodes from the original attribute space into the targeted subspace, a non-linear mapping is used. To make the mapped images respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace. Following [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec), attri2vec learns node representations by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
+    "\n",
+    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. Unlike the embeddings learned by DeepWalk/node2vec, the mapping is inductive. Different from the mapping generated by GraphSAGE, the mapping only performs on node content attributes and does not rely on any link information, which makes it possible to construct representations for new coming nodes having few or no link information from their content attributes.\n",
+    "\n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameters used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)`, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "\n",
+    "The entire model is trained end-to-end by minimizing the loss function of choice (e.g., binary cross-entropy between predicted node pair labels and true link labels) using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' links generated on demand and fed into the model.\n",
+    "\n",
+    "In this demo, we first train the attri2vec model on the in-sample subgraph and obtain a mapping function from node attributes to node representations, then apply the mapping function to the content attributes of out-of-sample nodes and obtain the representations of out-of-sample nodes. We evaluate the quality of inferred out-of-sample node representations by using it to predict the links of out-of-sample nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import stellargraph as sg\n",
+    "from stellargraph.data import UniformRandomWalk\n",
+    "from stellargraph.data import UnsupervisedSampler\n",
+    "from stellargraph.mapper import attri2vecLinkGenerator\n",
+    "\n",
+    "import keras \n",
+    "from keras.utils import Sequence\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input, Dense, Reshape, merge\n",
+    "from keras.layers.embeddings import Embedding\n",
+    "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import roc_auc_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading DBLP network data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset used in this demo can be downloaded from https://www.kaggle.com/daozhang/dblp-subgraph.\n",
+    "The following is the description of the dataset:\n",
+    "\n",
+    "> The content.txt file contains descriptions of the papers in the following format:\n",
+    "\n",
+    " \t\t<paper_id> <word_attributes> <class_label> <publication_year>\n",
+    "        \n",
+    "> The first entry in each line contains the unique integer ID (ranging from 0 to 18,447) of the paper followed by > binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last two entries in the line are the class label and the publication year of the paper.\n",
+    "> The edgeList.txt file contains the citation relations. Each line describes a link in the following format:\n",
+    "\t\t\n",
+    "        <ID of paper1> <ID of paper2>\n",
+    "        \n",
+    "> Each line contains two paper IDs, with paper2 citing paper1 or paper1 citing paper2.\n",
+    "\n",
+    "\n",
+    "Download and unzip the dblp-subgraph.zip file to a location on your computer and set the `data_dir` variable to\n",
+    "point to the location of the dataset (the \"DBLP\" directory containing \"content.txt\" and \"edgeList.txt\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"~/data/DBLP\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the graph from the edgelist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edgelist = pd.read_csv(os.path.join(data_dir, \"edgeList.txt\"), sep='\\t', header=None, names=[\"source\", \"target\"])\n",
+    "edgelist[\"label\"] = \"cites\"  # set the edge type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load paper content features, subjects and publishing years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_names = [\"w_{}\".format(ii) for ii in range(2476)]\n",
+    "node_column_names =  feature_names + [\"subject\", \"year\"]\n",
+    "node_data = pd.read_csv(os.path.join(data_dir, \"content.txt\"), sep='\\t', header=None, names=node_column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get DBLP Subgraph \n",
+    "### with papers published before a threshold year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the edge list connecting in-sample nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year_thresh = 2006 # the threshold year for in-sample and out-of-sample set split, which can be 2007, 2008 and 2009\n",
+    "subgraph_edgelist = []\n",
+    "for ii in range(len(edgelist)):\n",
+    "    source_index = edgelist[\"source\"][ii]\n",
+    "    target_index = edgelist[\"target\"][ii]\n",
+    "    source_year = int(node_data[\"year\"][source_index])\n",
+    "    target_year = int(node_data[\"year\"][target_index])\n",
+    "    if source_year < year_thresh and target_year < year_thresh:\n",
+    "        subgraph_edgelist.append([source_index, target_index])\n",
+    "subgraph_edgelist = pd.DataFrame(np.array(subgraph_edgelist), columns=[\"source\", \"target\"])\n",
+    "subgraph_edgelist[\"label\"] = \"cites\" # set the edge type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct the network from the selected edge list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Gnx = nx.from_pandas_edgelist(subgraph_edgelist, edge_attr=\"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(Gnx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get in-sample node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subgraph_node_data = node_data[node_data[\"year\"]<2006]\n",
+    "subgraph_node_features = subgraph_node_data[feature_names]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = sg.StellarGraph(Gnx, node_features=subgraph_node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 11776, Edges: 28937\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [11776]\n",
+      "    Edge types: paper-cites->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-cites->paper: [28937]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train attri2vec on the DBLP Subgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk, and random seed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = list(G.nodes())\n",
+    "number_of_walks = 1\n",
+    "length = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the batch size and the number of epochs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 50\n",
+    "epochs = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running attri2vecLinkGenerator with an estimated 2355 batches generated on the fly per epoch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_gen = attri2vecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following is the keras implementation of attri2vec. The objective attri2vec is equivalent to minimize the cross-entropy loss for predicting the context nodes with target nodes, with the predicted output being the dot product of the \"input embedding\" of target nodes and the \"output embedding\" of target nodes, followed by a sigmoid activation. The \"input embedding\", the objective node representation, is constructed from node content features through a linear transformation followed by a sigmoid activation. The \"out embedding\" is a look-up table for each context node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0807 17:47:53.407229 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n",
+      "W0807 17:47:53.423400 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "\n",
+      "W0807 17:47:53.428951 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "\n",
+      "W0807 17:47:53.504508 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n",
+      "W0807 17:47:53.510684 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+      "\n",
+      "W0807 17:47:53.517368 140736734368704 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    }
+   ],
+   "source": [
+    "embedding_size = 256 # set the embedding dimension to 256.\n",
+    "input_target = Input((G.node_feature_sizes()[\"paper\"],)) # define the input for target nodes as node content features \n",
+    "input_context = Input((1,)) # define the input for context nodes as node ids\n",
+    "\n",
+    "target = Dense(embedding_size, activation='sigmoid')(input_target) # get \"input embedding\" for the target node\n",
+    "target = Reshape((embedding_size, 1))(target) \n",
+    "out_embedding = Embedding(len(node_data), embedding_size, input_length=1, name='out_embedding')\n",
+    "context = out_embedding(input_context) # get the \"output embedding\" for the context node\n",
+    "context = Reshape((embedding_size, 1))(context)\n",
+    "\n",
+    "# perform dot product between the \"input embedding\" and the \"output embedding\"\n",
+    "dot_product = keras.layers.dot([target, context], axes = 1, normalize=False) \n",
+    "dot_product = Reshape((1,))(dot_product) \n",
+    "\n",
+    "output = Dense(1, activation='sigmoid')(dot_product) # get the predicted output\n",
+    "\n",
+    "model = Model(inputs=[input_target, input_context], outputs=output)\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
+    "    loss=keras.losses.binary_crossentropy,\n",
+    "    metrics=[keras.metrics.binary_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0807 17:47:56.943290 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/4\n",
+      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.7146 - binary_accuracy: 0.5173\n",
+      "Epoch 2/4\n",
+      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.6876 - binary_accuracy: 0.5374\n",
+      "Epoch 3/4\n",
+      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.6759 - binary_accuracy: 0.5468\n",
+      "Epoch 4/4\n",
+      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.6571 - binary_accuracy: 0.5578\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit_generator(\n",
+    "    train_gen,\n",
+    "    epochs=epochs,\n",
+    "    verbose=1,\n",
+    "    use_multiprocessing=False,\n",
+    "    workers=0,\n",
+    "    shuffle=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predicting links of out-of-sample nodes with the learned attri2vec model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node embeddings, for both in-sample and out-of-sample nodes, by applying the learned mapping function to node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_model = Model(inputs = input_target, outputs = Reshape((embedding_size,))(target))\n",
+    "node_embeddings = embedding_model.predict(node_data[feature_names].values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the positive and negative edges for in-sample nodes and out-of-sample nodes. The edges of the in-sample nodes only include the edges between in-sample nodes, and the edges of out-of-sample nodes are referred to all the edges linked to out-of-sample nodes, including the edges connecting in-sample and out-of-sample edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year_thresh = 2006\n",
+    "in_sample_edges = []\n",
+    "out_of_sample_edges = []\n",
+    "for ii in range(len(edgelist)):\n",
+    "    source_index = edgelist[\"source\"][ii]\n",
+    "    target_index = edgelist[\"target\"][ii]\n",
+    "    if source_index > target_index: # neglect edge direction for the undirected graph\n",
+    "        continue\n",
+    "    source_year = int(node_data[\"year\"][source_index])\n",
+    "    target_year = int(node_data[\"year\"][target_index])\n",
+    "    if source_year < year_thresh and target_year < year_thresh:\n",
+    "        in_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
+    "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
+    "        in_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative node\n",
+    "    else:\n",
+    "        out_of_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
+    "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
+    "        out_of_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative node\n",
+    "in_sample_edges = np.array(in_sample_edges)\n",
+    "out_of_sample_edges = np.array(out_of_sample_edges)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct the edge features from the learned node representations with l2 normed difference, where edge features are the element-wise square of the difference between the embeddings of two head nodes. Other strategy like element-wise product can also be used to construct edge features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_sample_edge_feat_from_emb = (node_embeddings[in_sample_edges[:,0]]-node_embeddings[in_sample_edges[:,1]])**2\n",
+    "out_of_sample_edge_feat_from_emb = (node_embeddings[out_of_sample_edges[:,0]]-node_embeddings[out_of_sample_edges[:,1]])**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the Logistic Regression classifier from in-sample edges with the edge features constructed from attri2vec embeddings. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
+       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf_edge_pred_from_emb = LogisticRegression(verbose=0, solver='lbfgs', multi_class=\"auto\", max_iter=500)\n",
+    "clf_edge_pred_from_emb.fit(in_sample_edge_feat_from_emb, in_sample_edges[:,2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict the edge existence probability with the trained Logistic Regression classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_pred_from_emb = clf_edge_pred_from_emb.predict_proba(out_of_sample_edge_feat_from_emb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the positive class index of `edge_pred_from_emb`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if clf_edge_pred_from_emb.classes_[0] == 1:\n",
+    "    positive_class_index = 0\n",
+    "else:\n",
+    "    positive_class_index = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the AUC score for the prediction with attri2vec embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.736614362152765"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(out_of_sample_edges[:,2], edge_pred_from_emb[:,positive_class_index])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the baseline, we also investigate the performance of node content features in predicting the edges of out-of-sample nodes. Firstly, we construct edge features from node content features with the same strategy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_sample_edge_rep_from_feat = (node_data[feature_names].values[in_sample_edges[:,0]]-node_data[feature_names].values[in_sample_edges[:,1]])**2\n",
+    "out_of_sample_edge_rep_from_feat = (node_data[feature_names].values[out_of_sample_edges[:,0]]-node_data[feature_names].values[out_of_sample_edges[:,1]])**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we train the Logistic Regression classifier from in-sample edges with the edge features constructed from node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
+       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf_edge_pred_from_feat = LogisticRegression(verbose=0, solver='lbfgs', multi_class=\"auto\", max_iter=500)\n",
+    "clf_edge_pred_from_feat.fit(in_sample_edge_rep_from_feat, in_sample_edges[:,2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict the edge existence probability with the trained Logistic Regression classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_pred_from_feat = clf_edge_pred_from_feat.predict_proba(out_of_sample_edge_rep_from_feat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get positive class index of `clf_edge_pred_from_feat`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if clf_edge_pred_from_feat.classes_[0] == 1:\n",
+    "    positive_class_index = 0\n",
+    "else:\n",
+    "    positive_class_index = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the AUC score for the prediction with node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6651658318918301"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(out_of_sample_edges[:,2], edge_pred_from_feat[:,positive_class_index])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "attri2vec performs much better than node content features in predicting the links of out-of-sample nodes."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 46a77dd4429925479d08229872e1a797794586c5 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 12:52:25 +1000
Subject: [PATCH 04/82] reformat link_mappers.py with black

---
 .DS_Store                           | Bin 0 -> 6148 bytes
 .gitignore                          |   1 +
 demos/.DS_Store                     | Bin 0 -> 6148 bytes
 requirements.txt                    |   1 -
 stellargraph/.DS_Store              | Bin 0 -> 6148 bytes
 stellargraph/mapper/link_mappers.py |  24 +++++++++++-------------
 6 files changed, 12 insertions(+), 14 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 demos/.DS_Store
 create mode 100644 stellargraph/.DS_Store

diff --git a/.DS_Store b/.DS_Store
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index 0000000000000000000000000000000000000000..a7c5630e292e8a22ff249f7a06d0ea30027f092a
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diff --git a/.gitignore b/.gitignore
index 96931b78f..08e33bc8b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -117,3 +117,4 @@ demos/graphsage-test/log
 
 # Vim session
 Session.vim
+*.txt
diff --git a/demos/.DS_Store b/demos/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..e03e0ae2fe6fa4a57617c72c9464fc7a55199887
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diff --git a/requirements.txt b/requirements.txt
index 0f8c4fe20..4ea9305e2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,4 +14,3 @@ pytest==3.9.3
 pytest-benchmark>=3.1
 pytest-cov>=2.6.0
 coveralls>=1.5.1
- 
diff --git a/stellargraph/.DS_Store b/stellargraph/.DS_Store
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diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
index 5b086cda3..a791fac85 100755
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -24,7 +24,7 @@
     "OnDemandLinkSequence",
     "GraphSAGELinkGenerator",
     "HinSAGELinkGenerator",
-    "attri2vecLinkGenerator"
+    "attri2vecLinkGenerator",
 ]
 
 import random
@@ -208,16 +208,16 @@ def __init__(self, generator, walker):
 
             if isinstance(self.generator, GraphSAGELinkGenerator):
                 print(
-                        "Running GraphSAGELinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
-                                round(self.data_size / self.generator.batch_size)
-                       )
-                     )
+                    "Running GraphSAGELinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
+                        round(self.data_size / self.generator.batch_size)
+                    )
+                )
             else:
                 print(
-                        "Running attri2vecLinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
-                                round(self.data_size / self.generator.batch_size)
-                       )
-                     )
+                    "Running attri2vecLinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
+                        round(self.data_size / self.generator.batch_size)
+                    )
+                )
 
             self._gen = self.walker.generator(
                 self.generator.batch_size
@@ -616,6 +616,7 @@ def flow(self, link_ids, targets=None, shuffle=False):
 
         return LinkSequence(self, link_ids, targets, shuffle)
 
+
 class attri2vecLinkGenerator:
     """
     A data generator for link prediction with Homogeneous attri2vec models
@@ -643,7 +644,7 @@ class attri2vecLinkGenerator:
         name, optional: Name of generator
     """
 
-    def __init__(self, G, batch_size, num_samples=[1,1], seed=None, name=None):
+    def __init__(self, G, batch_size, num_samples=[1, 1], seed=None, name=None):
         if not isinstance(G, StellarGraphBase):
             raise TypeError("Graph must be a StellarGraph object.")
 
@@ -657,9 +658,6 @@ def __init__(self, G, batch_size, num_samples=[1,1], seed=None, name=None):
         # We need a schema for compatibility with HinSAGE
         self.schema = G.create_graph_schema(create_type_maps=True)
 
-        # The sampler used to generate random samples of neighbours
-        #self.sampler = SampledBreadthFirstWalk(G, graph_schema=self.schema, seed=seed)
-
     def sample_features(self, head_links, sampling_schema):
         """
         Sample content features of the target nodes and the ids of the context nodes

From 189a6122aece4b9d3fe9090ee6f213a9dafe5355 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 13:03:10 +1000
Subject: [PATCH 05/82] Update .gitignore

---
 .gitignore | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 08e33bc8b..96931b78f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -117,4 +117,3 @@ demos/graphsage-test/log
 
 # Vim session
 Session.vim
-*.txt

From 158f32b6719b046bd3e02507b73f4716a953a630 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 13:09:43 +1000
Subject: [PATCH 06/82] Delete .DS_Store

---
 stellargraph/.DS_Store | Bin 6148 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 stellargraph/.DS_Store

diff --git a/stellargraph/.DS_Store b/stellargraph/.DS_Store
deleted file mode 100644
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GIT binary patch
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From 1c24d09321e9fadabf0579b43feeeca0f8c1f253 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 13:11:23 +1000
Subject: [PATCH 07/82] Delete .DS_Store

---
 .DS_Store | Bin 6148 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
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From c53a5cd2f7bd9ebc1067466f55b0bd57097a32e6 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 13:11:51 +1000
Subject: [PATCH 08/82] Delete .DS_Store

---
 demos/.DS_Store | Bin 6148 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 demos/.DS_Store

diff --git a/demos/.DS_Store b/demos/.DS_Store
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From 4517c9f9748aecb33cf54b58d5c03289934471b0 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 13:33:24 +1000
Subject: [PATCH 09/82] Update README.md

---
 demos/embeddings/README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/demos/embeddings/README.md b/demos/embeddings/README.md
index db2c667f7..7f8544371 100644
--- a/demos/embeddings/README.md
+++ b/demos/embeddings/README.md
@@ -4,11 +4,13 @@ This folder contains three [Jupyter](http://jupyter.org/) python notebooks demon
 
 **Node2Vec** and **Metapath2Vec** notebooks demonstrate the combined use of `stellargraph` and `Gensim` [4] libraries for representation learning on homogeneous and heterogeneous graphs. 
 **Unsupervised GraphSAGE** notebook demonstrates the use of `Stellargraph` library's GraphSAGE implementation for unsupervised learning of node embeddings for homogeneous graphs with node features.
+**attri2vec** notebook demonstrates the implementation of attri2vec with the `Stellargraph` library for unsupervised inductive learning of node embeddings for homogeneous graphs with node features, and the evaluation for its ablity to infer the representations of out-of-sample nodes with the out-of-sample node link prediction task.   
 
 The notebooks demonstrate the following algorithms.
 - `stellargraph-node2vec.ipynb` The **Node2Vec** algorithm [1] for representation learning on homogeneous graphs
 - `stellargraph-metapath2vec.ipynb` The **Metapath2Vec** algorithm [2] for representation learning on heterogeneous graphs.
 - `embeddings-unsupervised-graphsage-cora.ipynb` The **Unsupervised GraphSAGE** algorithm [5] for representation learning on homogeneous graphs with node features.
+- `stellargraph-attri2vec-DBLP.ipynb` The **attri2vec** algorithm [6] for representation learning on homogeneous graphs with node features.
 
 All examples demonstrate how to calculate embedding vectors for a graph's nodes in just a few lines of Python code. 
 The learned node representations can be used in numerous downstream tasks such as node attribute inference, link
@@ -32,3 +34,5 @@ G. S. Corrado, and J. Dean.  In Advances in Neural Information Processing System
 
 **5.** Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216
 [cs.SI], 2017. ([link](http://snap.stanford.edu/graphsage/))
+
+**6.** Attributed Network Embedding Via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))

From 947c843879eb4811e2c51a68483d7aa691e9a2b9 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 13:33:46 +1000
Subject: [PATCH 10/82] Update README.md

---
 demos/embeddings/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/embeddings/README.md b/demos/embeddings/README.md
index 7f8544371..b62eb6a0a 100644
--- a/demos/embeddings/README.md
+++ b/demos/embeddings/README.md
@@ -35,4 +35,4 @@ G. S. Corrado, and J. Dean.  In Advances in Neural Information Processing System
 **5.** Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216
 [cs.SI], 2017. ([link](http://snap.stanford.edu/graphsage/))
 
-**6.** Attributed Network Embedding Via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))
+**6.** Attributed Network Embedding via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))

From a37f67c3cc723b8b0c25e007f32fb70b77a4f0c9 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 8 Aug 2019 13:34:56 +1000
Subject: [PATCH 11/82] Update README.md

---
 demos/embeddings/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/embeddings/README.md b/demos/embeddings/README.md
index b62eb6a0a..18f4aa190 100644
--- a/demos/embeddings/README.md
+++ b/demos/embeddings/README.md
@@ -4,7 +4,7 @@ This folder contains three [Jupyter](http://jupyter.org/) python notebooks demon
 
 **Node2Vec** and **Metapath2Vec** notebooks demonstrate the combined use of `stellargraph` and `Gensim` [4] libraries for representation learning on homogeneous and heterogeneous graphs. 
 **Unsupervised GraphSAGE** notebook demonstrates the use of `Stellargraph` library's GraphSAGE implementation for unsupervised learning of node embeddings for homogeneous graphs with node features.
-**attri2vec** notebook demonstrates the implementation of attri2vec with the `Stellargraph` library for unsupervised inductive learning of node embeddings for homogeneous graphs with node features, and the evaluation for its ablity to infer the representations of out-of-sample nodes with the out-of-sample node link prediction task.   
+**attri2vec** notebook demonstrates the implementation of attri2vec with the `Stellargraph` library for unsupervised inductive learning of node embeddings for homogeneous graphs with node features, and the evaluation for its ability to infer the representations of out-of-sample nodes with the out-of-sample node link prediction task.   
 
 The notebooks demonstrate the following algorithms.
 - `stellargraph-node2vec.ipynb` The **Node2Vec** algorithm [1] for representation learning on homogeneous graphs

From 50ccc0cf7cfa25cb68f45c22459ef08cebcec483 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:08:58 +1000
Subject: [PATCH 12/82] rewrite the attri2vec demo by using the Attri2Vec
 model, Attri2VecNodeGenerator and Attri2VecLinkGenerator

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 342 +++++++++++++-----
 1 file changed, 256 insertions(+), 86 deletions(-)

diff --git a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
index 8abfad166..47aefb365 100644
--- a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
+++ b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Infer Representations for Out-of-sample Nodes Through attri2vec"
+    "# Inferring Representations for Out-of-sample Nodes Through attri2vec"
    ]
   },
   {
@@ -26,9 +26,9 @@
     "\n",
     "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. Unlike the embeddings learned by DeepWalk/node2vec, the mapping is inductive. Different from the mapping generated by GraphSAGE, the mapping only performs on node content attributes and does not rely on any link information, which makes it possible to construct representations for new coming nodes having few or no link information from their content attributes.\n",
     "\n",
-    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameters used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)`, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
     "\n",
-    "The entire model is trained end-to-end by minimizing the loss function of choice (e.g., binary cross-entropy between predicted node pair labels and true link labels) using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' links generated on demand and fed into the model.\n",
+    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model.\n",
     "\n",
     "In this demo, we first train the attri2vec model on the in-sample subgraph and obtain a mapping function from node attributes to node representations, then apply the mapping function to the content attributes of out-of-sample nodes and obtain the representations of out-of-sample nodes. We evaluate the quality of inferred out-of-sample node representations by using it to predict the links of out-of-sample nodes."
    ]
@@ -56,16 +56,13 @@
     "import stellargraph as sg\n",
     "from stellargraph.data import UniformRandomWalk\n",
     "from stellargraph.data import UnsupervisedSampler\n",
-    "from stellargraph.mapper import attri2vecLinkGenerator\n",
+    "from stellargraph.mapper import Attri2VecLinkGenerator\n",
+    "from stellargraph.mapper.node_mappers import Attri2VecNodeGenerator\n",
+    "from stellargraph.layer import Attri2Vec, link_classification\n",
     "\n",
-    "import keras \n",
-    "from keras.utils import Sequence\n",
-    "from keras.models import Model\n",
-    "from keras.layers import Input, Dense, Reshape, merge\n",
-    "from keras.layers.embeddings import Embedding\n",
+    "import keras\n",
     "\n",
     "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score\n",
     "from sklearn.metrics import roc_auc_score"
    ]
   },
@@ -87,7 +84,7 @@
     "\n",
     " \t\t<paper_id> <word_attributes> <class_label> <publication_year>\n",
     "        \n",
-    "> The first entry in each line contains the unique integer ID (ranging from 0 to 18,447) of the paper followed by > binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last two entries in the line are the class label and the publication year of the paper.\n",
+    "> The first entry in each line contains the unique integer ID (ranging from 0 to 18,447) of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last two entries in the line are the class label and the publication year of the paper.\n",
     "> The edgeList.txt file contains the citation relations. Each line describes a link in the following format:\n",
     "\t\t\n",
     "        <ID of paper1> <ID of paper2>\n",
@@ -143,6 +140,96 @@
     "node_data = pd.read_csv(os.path.join(data_dir, \"content.txt\"), sep='\\t', header=None, names=node_column_names)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct the whole graph from edge list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_all_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(G_all_nx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_node_features = node_data[feature_names]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_all = sg.StellarGraph(G_all_nx, node_features=all_node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 18448, Edges: 45611\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [18448]\n",
+      "    Edge types: paper-cites->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-cites->paper: [45611]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G_all.info())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -160,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,11 +273,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "Gnx = nx.from_pandas_edgelist(subgraph_edgelist, edge_attr=\"label\")"
+    "G_sub_nx = nx.from_pandas_edgelist(subgraph_edgelist, edge_attr=\"label\")"
    ]
   },
   {
@@ -202,11 +289,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "nx.set_node_attributes(Gnx, \"paper\", \"label\")"
+    "nx.set_node_attributes(G_sub_nx, \"paper\", \"label\")"
    ]
   },
   {
@@ -218,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,16 +322,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "G = sg.StellarGraph(Gnx, node_features=subgraph_node_features)"
+    "G_sub = sg.StellarGraph(G_sub_nx, node_features=subgraph_node_features)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -265,7 +352,7 @@
     }
    ],
    "source": [
-    "print(G.info())"
+    "print(G_sub.info())"
    ]
   },
   {
@@ -284,12 +371,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "nodes = list(G.nodes())\n",
-    "number_of_walks = 1\n",
+    "nodes = list(G_sub.nodes())\n",
+    "number_of_walks = 2\n",
     "length = 5"
    ]
   },
@@ -302,11 +389,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+    "unsupervised_samples = UnsupervisedSampler(G_sub, nodes=nodes, length=length, number_of_walks=number_of_walks)"
    ]
   },
   {
@@ -318,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,36 +417,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Define the attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
+    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running attri2vecLinkGenerator with an estimated 2355 batches generated on the fly per epoch.\n"
+      "Running Attri2VecLinkGenerator with an estimated 4710 batches generated on the fly per epoch.\n"
      ]
     }
    ],
    "source": [
-    "train_gen = attri2vecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
+    "train_gen = Attri2VecLinkGenerator(G_sub, batch_size).flow(unsupervised_samples)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following is the keras implementation of attri2vec. The objective attri2vec is equivalent to minimize the cross-entropy loss for predicting the context nodes with target nodes, with the predicted output being the dot product of the \"input embedding\" of target nodes and the \"output embedding\" of target nodes, followed by a sigmoid activation. The \"input embedding\", the objective node representation, is constructed from node content features through a linear transformation followed by a sigmoid activation. The \"out embedding\" is a look-up table for each context node."
+    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
+    "\n",
+    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term, no dropout and no normalization. (Dropout can be switched on by specifying a positive dropout rate, 0 < dropout < 1 and normalization can be set to 'l2'). "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -367,40 +456,94 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0807 17:47:53.407229 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
-      "\n",
-      "W0807 17:47:53.423400 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
-      "\n",
-      "W0807 17:47:53.428951 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "W0821 16:57:15.264071 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_sizes = [128]\n",
+    "attri2vec = Attri2Vec(\n",
+    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, dropout=0.0, normalize=\"None\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0821 16:57:18.045172 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
       "\n",
-      "W0807 17:47:53.504508 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "W0821 16:57:18.051191 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
+    "x_inp, x_out = attri2vec.build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = link_classification(\n",
+    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
+    ")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0821 16:57:23.894735 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
       "\n",
-      "W0807 17:47:53.510684 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+      "W0821 16:57:23.901146 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
       "\n",
-      "W0807 17:47:53.517368 140736734368704 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0821 16:57:23.909626 140736102417344 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
     }
    ],
    "source": [
-    "embedding_size = 256 # set the embedding dimension to 256.\n",
-    "input_target = Input((G.node_feature_sizes()[\"paper\"],)) # define the input for target nodes as node content features \n",
-    "input_context = Input((1,)) # define the input for context nodes as node ids\n",
-    "\n",
-    "target = Dense(embedding_size, activation='sigmoid')(input_target) # get \"input embedding\" for the target node\n",
-    "target = Reshape((embedding_size, 1))(target) \n",
-    "out_embedding = Embedding(len(node_data), embedding_size, input_length=1, name='out_embedding')\n",
-    "context = out_embedding(input_context) # get the \"output embedding\" for the context node\n",
-    "context = Reshape((embedding_size, 1))(context)\n",
+    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
     "\n",
-    "# perform dot product between the \"input embedding\" and the \"output embedding\"\n",
-    "dot_product = keras.layers.dot([target, context], axes = 1, normalize=False) \n",
-    "dot_product = Reshape((1,))(dot_product) \n",
-    "\n",
-    "output = Dense(1, activation='sigmoid')(dot_product) # get the predicted output\n",
-    "\n",
-    "model = Model(inputs=[input_target, input_context], outputs=output)\n",
     "model.compile(\n",
     "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
     "    loss=keras.losses.binary_crossentropy,\n",
@@ -417,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {
     "scrolled": true
    },
@@ -426,7 +569,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0807 17:47:56.943290 140736734368704 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
+      "W0821 16:57:27.992636 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
       "\n"
      ]
     },
@@ -435,13 +578,13 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/4\n",
-      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.7146 - binary_accuracy: 0.5173\n",
+      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.6506 - binary_accuracy: 0.6515\n",
       "Epoch 2/4\n",
-      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.6876 - binary_accuracy: 0.5374\n",
+      "4711/4711 [==============================] - 94s 20ms/step - loss: 0.5718 - binary_accuracy: 0.6775\n",
       "Epoch 3/4\n",
-      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.6759 - binary_accuracy: 0.5468\n",
+      "4711/4711 [==============================] - 95s 20ms/step - loss: 0.4567 - binary_accuracy: 0.7736\n",
       "Epoch 4/4\n",
-      "2356/2356 [==============================] - 136s 58ms/step - loss: 0.6571 - binary_accuracy: 0.5578\n"
+      "4711/4711 [==============================] - 94s 20ms/step - loss: 0.2897 - binary_accuracy: 0.8791\n"
      ]
     }
    ],
@@ -450,8 +593,8 @@
     "    train_gen,\n",
     "    epochs=epochs,\n",
     "    verbose=1,\n",
-    "    use_multiprocessing=False,\n",
-    "    workers=0,\n",
+    "    use_multiprocessing=True,\n",
+    "    workers=4,\n",
     "    shuffle=True,\n",
     ")"
    ]
@@ -467,17 +610,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Get the node embeddings, for both in-sample and out-of-sample nodes, by applying the learned mapping function to node content features."
+    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "embedding_model = Model(inputs = input_target, outputs = Reshape((embedding_size,))(target))\n",
-    "node_embeddings = embedding_model.predict(node_data[feature_names].values)"
+    "x_inp_src = x_inp[0]\n",
+    "x_out_src = x_out[0]\n",
+    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node embeddings, for both in-sample and out-of-sample nodes, by applying the learned mapping function to node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "369/369 [==============================] - 1s 2ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_ids = node_data.index\n",
+    "node_gen = Attri2VecNodeGenerator(G_all, batch_size).flow(node_ids)\n",
+    "node_embeddings = embedding_model.predict_generator(node_gen, workers=4, verbose=1)"
    ]
   },
   {
@@ -489,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -524,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -554,7 +724,7 @@
        "                   warm_start=False)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,7 +759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -608,16 +778,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.736614362152765"
+       "0.8109590038749268"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -635,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -652,7 +822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -665,7 +835,7 @@
        "                   warm_start=False)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -684,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -700,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -719,16 +889,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6651658318918301"
+       "0.6638026335702683"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }

From fb7ce3694e1bc8b9490f31d09692b51737c00d16 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:10:48 +1000
Subject: [PATCH 13/82] Add the module to define Attri2Vec class

---
 stellargraph/layer/attri2vec.py | 212 ++++++++++++++++++++++++++++++++
 1 file changed, 212 insertions(+)
 create mode 100644 stellargraph/layer/attri2vec.py

diff --git a/stellargraph/layer/attri2vec.py b/stellargraph/layer/attri2vec.py
new file mode 100644
index 000000000..ec4774357
--- /dev/null
+++ b/stellargraph/layer/attri2vec.py
@@ -0,0 +1,212 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2018 Data61, CSIRO
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+attri2vec
+
+"""
+__all__ = ["Attri2Vec"]
+
+from keras import Input
+from keras import backend as K
+from keras.layers import Input, Dense, Lambda, Dropout, Reshape
+from keras.layers.embeddings import Embedding
+import warnings
+
+
+class Attri2Vec:
+    """
+    Implementation of the attri2vec algorithm of Zhang et al. with Keras layers.
+    see: https://arxiv.org/abs/1901.04095
+
+    The model minimally requires specification of the layer sizes as a list of ints
+    corresponding to the feature dimensions for each hidden layer and a generator object.
+
+    Args:
+        layer_sizes (list): Hidden feature dimensions for each layer
+        generator (Sequence): A NodeSequence or LinkSequence. 
+        input_dim (int): The dimensions of the node features used as input to the model.
+        node_num (int): The number of nodes in the given graph.
+        bias (bool): If True a bias vector is learnt for each layer in the attri2vec model, default to False.
+        dropout (float): The dropout supplied to each layer in the attri2vec model, default to 0.0.
+        normalize (str or None): The normalization used after each layer, default to None.
+
+    """
+
+    def __init__(
+        self,
+        layer_sizes,
+        generator=None,
+        input_dim=None,
+        node_num=None,
+        bias=False,
+        dropout=0.0,
+        normalize=None,
+    ):
+
+        if normalize == "l2":
+            self._normalization = Lambda(lambda x: K.l2_normalize(x, axis=-1))
+
+        elif normalize is None or normalize == "none" or normalize == "None":
+            self._normalization = Lambda(lambda x: x)
+
+        else:
+            raise ValueError(
+                "Normalization should be either 'l2' or 'none'; received '{}'".format(
+                    normalize
+                )
+            )
+
+        # Get the input_dim and node_num from the generator if it is given
+        # Use both the schema and head node type from the generator
+        self.generator = generator
+        if generator is not None:
+            feature_sizes = generator.generator.graph.node_feature_sizes()
+            if len(feature_sizes) > 1:
+                raise RuntimeError(
+                    "Attri2Vec called on graph with more than one node type."
+                )
+
+            self.input_feature_size = feature_sizes.popitem()[1]
+            self.input_node_num = generator.generator.graph.number_of_nodes()
+
+        elif input_dim is not None and node_num is not None:
+            self.input_feature_size = input_dim
+            self.input_node_num = node_num
+
+        else:
+            raise RuntimeError(
+                "If generator is not provided, input_dim and node_num must be specified."
+            )
+
+        # Model parameters
+        self.n_layers = len(layer_sizes)
+        self.bias = bias
+        self.dropout = dropout
+        self.activation = "sigmoid"  # the activation fuction used in hidden layers
+        self.initializer = (
+            "glorot_uniform"
+        )  # the initializer for the weights to construct hidden layers
+
+        # Feature dimensions for each layer
+        self.dims = [self.input_feature_size] + layer_sizes
+
+    def __call__(self, xin):
+        """
+        Construct node representations from node attributes through deep neural network
+
+        Args:
+            xin (Keras Tensor): Batch input features
+
+        Returns:
+            Output tensor
+        """
+        # Form Attri2Vec layers iteratively
+        h_layer = xin
+        for layer in range(0, self.n_layers):
+            h_layer = Dropout(self.dropout)(h_layer)
+            h_layer = Dense(
+                self.dims[layer + 1],
+                activation=self.activation,
+                kernel_initializer=self.initializer,
+                use_bias=self.bias,
+            )(h_layer)
+            h_layer = self._normalization(h_layer)
+
+        return h_layer
+
+    def node_model(self):
+        """
+        Builds a Attri2Vec model for node prediction
+
+        Returns:
+            tuple: (x_inp, x_out) where ``x_inp`` is a Keras input tensor
+            for the Attri2Vec model and ``x_out`` is the Keras tensor
+            for the Attri2Vec model output.
+
+        """
+        # Create tensor inputs
+        x_inp = Input(shape=(self.input_feature_size,))
+
+        # Output from Attri2Vec model
+        x_out = self(x_inp)
+
+        return x_inp, x_out
+
+    def link_model(self):
+        """
+        Builds a Attri2Vec model for link or node pair prediction
+
+        Returns:
+            tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors for (src, dst) nodes in the node pairs
+            and ``x_out`` is a list of output tensors for (src, dst) nodes in the node pairs
+
+        """
+        # Expose input and output sockets of the model, for source node:
+        x_inp_src, x_out_src = self.node_model()
+
+        # Expose input and out sockets of the model, for target node:
+        x_inp_dst = Input(shape=(1,))
+        output_embedding = Embedding(
+            self.input_node_num,
+            self.dims[self.n_layers],
+            input_length=1,
+            name="output_embedding",
+        )
+        x_out_dst = output_embedding(x_inp_dst)
+        x_out_dst = Reshape((self.dims[self.n_layers],))(x_out_dst)
+
+        x_inp = [x_inp_src, x_inp_dst]
+        x_out = [x_out_src, x_out_dst]
+        return x_inp, x_out
+
+    def build(self):
+        """
+        Builds a Attri2Vec model for node or link/node pair prediction, depending on the generator used to construct
+        the model (whether it is a node or link/node pair generator).
+
+        Returns:
+            tuple: (x_inp, x_out), where ``x_inp`` contains Keras input tensor(s)
+            for the specified Attri2Vec model (either node or link/node pair model) and ``x_out`` contains
+            model output tensor(s) of shape (batch_size, layer_sizes[-1])
+
+        """
+        if self.generator is not None and hasattr(self.generator, "_sampling_schema"):
+            if len(self.generator._sampling_schema) == 1:
+                return self.node_model()
+            elif len(self.generator._sampling_schema) == 2:
+                return self.link_model()
+            else:
+                raise RuntimeError(
+                    "The generator used for model creation is neither a node nor a link generator, "
+                    "unable to figure out how to build the model. Consider using node_model or "
+                    "link_model method explicitly to build node or link prediction model, respectively."
+                )
+        else:
+            raise RuntimeError(
+                "Suitable generator is not provided at model creation time, unable to figure out how to build the model. "
+                "Consider either providing a generator, or using node_model or link_model method explicitly to build node or "
+                "link prediction model, respectively."
+            )
+
+    def default_model(self, flatten_output=True):
+        warnings.warn(
+            "The .default_model() method will be deprecated in future versions. "
+            "Please use .build() method instead.",
+            PendingDeprecationWarning,
+        )
+        return self.build()

From 094650e713b2ad7f726f10aa2ccf90759ea7c0dc Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:12:43 +1000
Subject: [PATCH 14/82] Add the definition of the class: Attri2VecLinkGenerator

---
 stellargraph/mapper/link_mappers.py | 49 +++++++++++++++--------------
 1 file changed, 26 insertions(+), 23 deletions(-)

diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
index a791fac85..1f3e5b750 100755
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -16,7 +16,7 @@
 
 """
 Generators that create batches of data from a machine-learnign ready graph
-for link prediction/link attribute inference problems using GraphSAGE and HinSAGE.
+for link prediction/link attribute inference problems using GraphSAGE, HinSAGE and Attri2Vec.
 
 """
 __all__ = [
@@ -24,7 +24,7 @@
     "OnDemandLinkSequence",
     "GraphSAGELinkGenerator",
     "HinSAGELinkGenerator",
-    "attri2vecLinkGenerator",
+    "Attri2VecLinkGenerator",
 ]
 
 import random
@@ -56,11 +56,12 @@ class LinkSequence(Sequence):
     :meth:`keras.Model.evaluate_generator`, and :meth:`keras.Model.predict_generator`
     This class generates data samples for link inference models
     and should be created using the :meth:`flow` method of
-    :class:`GraphSAGELinkGenerator` or :class:`HinSAGELinkGenerator` .
+    :class:`GraphSAGELinkGenerator` or :class:`HinSAGELinkGenerator` or :class:`Attri2VecLinkGenerator`.
     Args:
-        generator: An instance of :class:`GraphSAGELinkGenerator` or :class:`HinSAGELinkGenerator`.
+        generator: An instance of :class:`GraphSAGELinkGenerator` or :class:`HinSAGELinkGenerator` or 
+        :class:`Attri2VecLinkGenerator`.
         ids (list or iterable): Link IDs to batch, each link id being a tuple of (src, dst) node ids.
-            (The graph nodes must have a "feature" attribute that is used as input to the GraphSAGE model.)
+            (The graph nodes must have a "feature" attribute that is used as input to the GraphSAGE/Attri2Vec model.)
             These are the links that are to be used to train or inference, and the embeddings
             calculated for these links via a binary operator applied to their source and destination nodes,
             are passed to the downstream task of link prediction or link attribute inference.
@@ -176,10 +177,10 @@ class OnDemandLinkSequence(Sequence):
 
     This class generates data samples for link inference models
     and should be created using the :meth:`flow` method of
-    :class:`GraphSAGELinkGenerator` ` .
+    :class:`GraphSAGELinkGenerator` or :class:`Attri2VecLinkGenerator`.
 
     Args:
-        generator: An instance of :class:`GraphSAGELinkGenerator` or 'attri2vecLink Generator'.
+        generator: An instance of :class:`GraphSAGELinkGenerator` or 'Attri2VecLinkGenerator'.
         sampler:  An instance of :class:`UnsupervisedSampler` that encapsulates the neighbourhood sampling of a graph.
         The generator method of this class returns `batch_size` of positive and negative samples on demand.
     """
@@ -214,7 +215,7 @@ def __init__(self, generator, walker):
                 )
             else:
                 print(
-                    "Running attri2vecLinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
+                    "Running Attri2VecLinkGenerator with an estimated {} batches generated on the fly per epoch.".format(
                         round(self.data_size / self.generator.batch_size)
                     )
                 )
@@ -617,11 +618,11 @@ def flow(self, link_ids, targets=None, shuffle=False):
         return LinkSequence(self, link_ids, targets, shuffle)
 
 
-class attri2vecLinkGenerator:
+class Attri2VecLinkGenerator:
     """
-    A data generator for link prediction with Homogeneous attri2vec models
+    A data generator for link/node pair prediction with attri2vec models
 
-    At minimum, supply the StellarGraph the batch size.
+    At minimum, supply the StellarGraph and the batch size.
 
     The supplied graph should be a StellarGraph object that is ready for
     machine learning. Currently the model requires node features for all
@@ -633,25 +634,23 @@ class attri2vecLinkGenerator:
 
     Example::
 
-        G_generator = attri2vecLinkGenerator(G, 50)
+        G_generator = Attri2VecLinkGenerator(G, 50)
         train_data_gen = G_generator.flow(edge_ids)
 
     Args:
         G (StellarGraph): A machine-learning ready graph.
         batch_size (int): Size of batch of links to return.
-        num_samples: for compatibility with GraphSAGE
-        seed (int or str), optional: Random seed for the sampling methods.
-        name, optional: Name of generator
+        name, optional: Name of generator.
     """
 
-    def __init__(self, G, batch_size, num_samples=[1, 1], seed=None, name=None):
+    def __init__(self, G, batch_size, name=None):
         if not isinstance(G, StellarGraphBase):
             raise TypeError("Graph must be a StellarGraph object.")
 
         G.check_graph_for_ml(features=True)
 
         self.graph = G
-        self.num_samples = num_samples
+        self.num_samples = [0]  # for compatibility with GraphSAGE
         self.batch_size = batch_size
         self.name = name
 
@@ -665,16 +664,16 @@ def sample_features(self, head_links, sampling_schema):
 
         Args:
             head_links: An iterable of edges to perform sampling for.
-            sampling_schema: The sampling schema for the model, for compatibility
-            with GraphSAGE and HinSAGE
+            sampling_schema: The sampling schema for the model, for compatibility with GraphSAGE and HinSAGE.
 
         Returns:
-            a list of feaure arrys, with each element being the feature of a
-            target node and the id of the corresponding context node
+            A list of feaure arrys, with each element being the feature of a
+            target node and the id of the corresponding context node.
         """
 
+        all_nodes = list(self.graph.nodes())
         target_ids = [head_link[0] for head_link in head_links]
-        context_ids = [head_link[1] for head_link in head_links]
+        context_ids = [all_nodes.index(head_link[1]) for head_link in head_links]
         target_feats = self.graph.get_feature_for_nodes(target_ids)
         batch_feats = [target_feats, np.array(context_ids)]
 
@@ -702,7 +701,7 @@ def flow(self, link_ids, targets=None, shuffle=False):
                 specifying the edges or an UnsupervisedSampler object that has a generator
                 method to generate samples on the fly.
             targets (optional, array): a 2D array of numeric targets with shape
-                `(len(link_ids), target_size)`
+                `(len(link_ids), target_size)`.
             shuffle (optional, bool): If True the node_ids will be shuffled at each
                 epoch, if False the node_ids will be processed in order.
 
@@ -713,6 +712,10 @@ def flow(self, link_ids, targets=None, shuffle=False):
         if isinstance(link_ids, UnsupervisedSampler):
             return OnDemandLinkSequence(self, link_ids)
 
+        # Otherwise pass iterable to standard LinkSequence
+        elif isinstance(link_ids, collections.Iterable):
+            return LinkSequence(self, link_ids, targets, shuffle)
+
         else:
             raise TypeError(
                 "Argument to .flow not recognised. "

From b9a81c976250eb9712fe42bae907217a980d55c5 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:13:14 +1000
Subject: [PATCH 15/82] Add the definition of the class: Attri2VecNodeGenerator

---
 stellargraph/mapper/node_mappers.py | 142 ++++++++++++++++++++++++++--
 1 file changed, 133 insertions(+), 9 deletions(-)
 mode change 100644 => 100755 stellargraph/mapper/node_mappers.py

diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py
old mode 100644
new mode 100755
index 1e9518b9c..b94fc0db4
--- a/stellargraph/mapper/node_mappers.py
+++ b/stellargraph/mapper/node_mappers.py
@@ -22,6 +22,7 @@
     "NodeSequence",
     "GraphSAGENodeGenerator",
     "HinSAGENodeGenerator",
+    "Attri2VecNodeGenerator",
     "FullBatchNodeGenerator",
     "FullBatchNodeSequence",
     "SparseFullBatchNodeSequence",
@@ -54,17 +55,22 @@ class NodeSequence(Sequence):
 
     This class generated data samples for node inference models
     and should be created using the `.flow(...)` method of
-    :class:`GraphSAGENodeGenerator` or :class:`HinSAGENodeGenerator`.
-
-    These Generators are classes that capture the graph structure
-    and the feature vectors of each node. These generator classes
-    are used within the NodeSequence to generate samples of k-hop
-    neighbourhoods in the graph and to return to this class the
-    features from the sampled neighbourhoods.
+    :class:`GraphSAGENodeGenerator` or :class:`HinSAGENodeGenerator` 
+    or :class:`Attri2vecNodeGenerator`.
+
+    GraphSAGENodeGenerator and HinSAGENodeGenerator are classes that 
+    capture the graph structure and the feature vectors of each node. 
+    These generator classes are used within the NodeSequence to generate
+    samples of k-hop neighbourhoods in the graph and to return to this 
+    class the features from the sampled neighbourhoods.
+    
+    Attri2VecNodeGenerator is the class that captures node feature vectors
+    of each node.
 
     Args:
-        generator: GraphSAGENodeGenerator or HinSAGENodeGenerator
-            The generator object containing the graph information.
+        generator: GraphSAGENodeGenerator or HinSAGENodeGenerator or
+            Attri2VecNodeGenerator. The generator object containing the 
+            graph information.
         ids: list
             A list of the node_ids to be used as head-nodes in the
             downstream task.
@@ -508,6 +514,124 @@ def flow_from_dataframe(self, node_targets, shuffle=False):
         )
 
 
+class Attri2VecNodeGenerator:
+    """
+    A data generator for node prediction with attri2vec models
+
+    At minimum, supply the StellarGraph and the batch size.
+
+    The supplied graph should be a StellarGraph object that is ready for
+    machine learning. Currently the model requires node features for all
+    nodes in the graph.
+
+    Use the :meth:`flow` method supplying the nodes and (optionally) targets
+    to get an object that can be used as a Keras data generator.
+
+    Example::
+
+        G_generator = Attri2VecNodeGenerator(G, 50)
+        train_data_gen = G_generator.flow(train_node_ids, train_node_labels)
+        test_data_gen = G_generator.flow(test_node_ids)
+
+    Args:
+        G (StellarGraph): The machine-learning ready graph.
+        batch_size (int): Size of batch to return.
+        name (str or None): Name of the generator (optional).
+    """
+
+    def __init__(self, G, batch_size, name=None):
+        if not isinstance(G, StellarGraphBase):
+            raise TypeError("Graph must be a StellarGraph object.")
+
+        self.graph = G
+        self.num_samples = [0]  # for compatibility with GraphSAGE
+        self.batch_size = batch_size
+        self.name = name
+
+        # Check if the graph has features
+        G.check_graph_for_ml()
+
+        # We need a schema for compatibility with HinSAGE
+        self.schema = G.create_graph_schema(create_type_maps=True)
+
+        # Check that there is only a single node type for Attri2Vec
+        if len(self.schema.node_types) > 1:
+            print(
+                "Warning: running homogeneous Attri2Vec on a graph with multiple node types"
+            )
+
+    def sample_features(self, head_nodes, sampling_schema):
+        """
+        Sample content features of the head nodes, and return these as a list of feature 
+        arrays for the attri2vec algorithm.
+
+        Args:
+            head_nodes: An iterable of head nodes to perform sampling on.
+            sampling_schema: The sampling schema for the model, for compatibility with GraphSAGE and HinSAGE.
+
+        Returns:
+            A list of feaure arrys, with each element being the feature of a
+            head node.
+        """
+
+        batch_feats = self.graph.get_feature_for_nodes(head_nodes)
+        return batch_feats
+
+    def flow(self, node_ids, targets=None, shuffle=False):
+        """
+        Creates a generator/sequence object for training or evaluation
+        with the supplied node ids and numeric targets.
+
+        The node IDs are the nodes to train or inference on: the embeddings
+        calculated for these nodes are passed to the downstream task. These
+        are a subset of the nodes in the graph.
+
+        The targets are an array of numeric targets corresponding to the
+        supplied node_ids to be used by the downstream task. They should
+        be given in the same order as the list of node IDs.
+        If they are not specified (for example, for use in prediction),
+        the targets will not be available to the downsteam task.
+
+        Note that the shuffle argument should be True for training and
+        False for prediction.
+
+        Args:
+            node_ids: an iterable of node IDs.
+            targets: a 2D array of numeric targets with shape
+                `(len(node_ids), target_size)`.
+            shuffle (bool): If True the node_ids will be shuffled at each
+                epoch, if False the node_ids will be processed in order.
+
+        Returns:
+            A NodeSequence object to use with the Attri2Vec model
+            in Keras methods ``fit_generator``, ``evaluate_generator``,
+            and ``predict_generator``.
+
+        """
+        return NodeSequence(self, node_ids, targets, shuffle=shuffle)
+
+    def flow_from_dataframe(self, node_targets, shuffle=False):
+        """
+        Creates a generator/sequence object for training or evaluation
+        with the supplied node ids and numeric targets.
+
+        Args:
+            node_targets: a Pandas DataFrame of numeric targets indexed
+                by the node ID for that target.
+            shuffle (bool): If True the node_ids will be shuffled at each
+                epoch, if False the node_ids will be processed in order.
+
+        Returns:
+            A NodeSequence object to use with the Attri2Vec model
+            in Keras methods ``fit_generator``, ``evaluate_generator``,
+            and ``predict_generator``.
+
+        """
+        return NodeSequence(
+            self, node_targets.index, node_targets.values, shuffle=shuffle
+        )
+
+
 class SparseFullBatchNodeSequence(Sequence):
     """
     Keras-compatible data generator for for node inference models

From 04dbc46b3ed587848263bec4b0e65da998d22dc6 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:14:55 +1000
Subject: [PATCH 16/82] Add the test for Attri2VecNodeGenerator

---
 tests/mapper/test_node_mappers.py | 153 ++++++++++++++++++++++++++++++
 1 file changed, 153 insertions(+)
 mode change 100644 => 100755 tests/mapper/test_node_mappers.py

diff --git a/tests/mapper/test_node_mappers.py b/tests/mapper/test_node_mappers.py
old mode 100644
new mode 100755
index 39c66dc24..9002bf32b
--- a/tests/mapper/test_node_mappers.py
+++ b/tests/mapper/test_node_mappers.py
@@ -682,6 +682,159 @@ def test_hinnodemapper_no_neighbors():
     assert np.all(batch_feats[3][:, 0, 0] == np.array([12, 0, 0]))
 
 
+def test_attri2vec_nodemapper_constructor_nx():
+    """
+    Attri2VecNodeGenerator requires a StellarGraph object
+    """
+    G = nx.Graph()
+    G.add_nodes_from(range(4))
+
+    with pytest.raises(TypeError):
+        Attri2VecNodeGenerator(G, batch_size=2)
+
+
+def test_attri2vec_nodemapper_constructor_no_feats():
+    """
+    Attri2VecNodeGenerator requires the graph to have features
+    """
+
+    G = example_graph_1()
+    with pytest.raises(RuntimeError):
+        Attri2VecNodeGenerator(G, batch_size=2)
+
+
+def test_attri2vec_nodemapper_constructor():
+    n_feat = 4
+
+    G = example_graph_1(feature_size=n_feat)
+
+    generator = Attri2VecNodeGenerator(G, batch_size=2)
+
+    mapper = generator.flow(list(G))
+
+    assert generator.batch_size == 2
+    assert mapper.data_size == 4
+    assert len(mapper.ids) == 4
+
+
+def test_attri2vec_nodemapper_1():
+    n_feat = 4
+    n_batch = 2
+
+    # test graph
+    G1 = example_graph_1(n_feat)
+
+    mapper1 = Attri2VecNodeGenerator(G1, batch_size=n_batch).flow(G1.nodes())
+    assert len(mapper1) == 2
+
+    G2 = example_graph_2(n_feat)
+
+    mapper2 = Attri2VecNodeGenerator(G2, batch_size=n_batch).flow(G2.nodes())
+    assert len(mapper2) == 3
+
+    for mapper in [mapper1, mapper2]:
+        for ii in range(2):
+            nf, nl = mapper[ii]
+            assert nf.shape == (n_batch, n_feat)
+            assert nl is None
+
+    # Check beyond the graph lengh
+    with pytest.raises(IndexError):
+        nf, nl = mapper1[len(mapper1)]
+
+    # Check the last batch
+    nf, nl = mapper2[len(mapper2) - 1]
+    assert nf.shape == (1, n_feat)
+
+    # This will fail as the nodes are not in the graph
+    with pytest.raises(KeyError):
+        Attri2VecNodeGenerator(G1, batch_size=2).flow(["A", "B"])
+
+
+def test_attri2vec_nodemapper_shuffle():
+    n_feat = 1
+    n_batch = 2
+
+    G = example_graph_2(feature_size=n_feat)
+    nodes = list(G.nodes())
+
+    # With shuffle
+    random.seed(15)
+    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
+        nodes, nodes, shuffle=True
+    )
+
+    expected_node_batches = [[5, 4], [3, 1], [2]]
+    assert len(mapper) == 3
+    for ii in range(len(mapper)):
+        nf, nl = mapper[ii]
+        assert all(np.ravel(nf) == expected_node_batches[ii])
+        assert all(np.array(nl) == expected_node_batches[ii])
+
+    # This should re-shuffle the IDs
+    mapper.on_epoch_end()
+    expected_node_batches = [[4, 3], [1, 5], [2]]
+    assert len(mapper) == 3
+    for ii in range(len(mapper)):
+        nf, nl = mapper[ii]
+        assert all(np.ravel(nf) == expected_node_batches[ii])
+        assert all(np.array(nl) == expected_node_batches[ii])
+
+    # With no shuffle
+    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
+        nodes, nodes, shuffle=False
+    )
+    expected_node_batches = [[1, 2], [3, 4], [5]]
+    assert len(mapper) == 3
+    for ii in range(len(mapper)):
+        nf, nl = mapper[ii]
+        assert all(np.ravel(nf) == expected_node_batches[ii])
+        assert all(np.array(nl) == expected_node_batches[ii])
+
+
+def test_attri2vec_nodemapper_with_labels():
+    n_feat = 4
+    n_batch = 2
+
+    # test graph
+    G2 = example_graph_2(n_feat)
+    nodes = list(G2)
+    labels = [n * 2 for n in nodes]
+
+    gen = Attri2VecNodeGenerator(G2, batch_size=n_batch).flow(nodes, labels)
+    assert len(gen) == 3
+
+    for ii in range(3):
+        nf, nl = gen[ii]
+
+        # Check sizes - note batch sizes are (2,2,1) for each iteration
+        assert nf.shape[1:] == (n_feat,)
+
+        # Check labels
+        assert all(int(a) == int(2 * b) for a, b in zip(nl, nf[:, 0]))
+
+    # Check beyond the graph lengh
+    with pytest.raises(IndexError):
+        nf, nl = gen[len(gen)]
+
+
+def test_attri2vec_nodemapper_incorrect_targets():
+    """
+    Tests checks on target shape
+    """
+    n_feat = 4
+    n_batch = 2
+
+    # test graph
+    G = example_graph_1(feature_size=n_feat)
+
+    with pytest.raises(TypeError):
+        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), 1)
+
+    with pytest.raises(ValueError):
+        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), targets=[])
+
+
 def create_graph_features():
     G = nx.Graph()
     G.add_nodes_from(["a", "b", "c"])

From 3e906417312a6e5e539ccedfe9dd04218f0b1557 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:15:17 +1000
Subject: [PATCH 17/82] Add test for Attri2VecLinkGenerator

---
 tests/mapper/test_link_mappers.py | 151 ++++++++++++++++++++++++++++++
 1 file changed, 151 insertions(+)
 mode change 100644 => 100755 tests/mapper/test_link_mappers.py

diff --git a/tests/mapper/test_link_mappers.py b/tests/mapper/test_link_mappers.py
old mode 100644
new mode 100755
index 7a033a623..b4a428f11
--- a/tests/mapper/test_link_mappers.py
+++ b/tests/mapper/test_link_mappers.py
@@ -695,3 +695,154 @@ def test_HinSAGELinkGenerator_isolates(self):
 
         # With two isolates, all features are zero
         assert all(pytest.approx(0) == x for x in ne[2:])
+
+
+class Test_Attri2VecLinkGenerator:
+    """
+    Tests of Attri2VecLinkGenerator class
+    """
+
+    n_feat = 4
+    batch_size = 2
+
+    def test_LinkMapper_constructor(self):
+
+        G = example_Graph_1(self.n_feat)
+        edge_labels = [0] * G.number_of_edges()
+
+        generator = Attri2VecLinkGenerator(G, batch_size=self.batch_size)
+        mapper = generator.flow(G.edges(), edge_labels)
+        assert generator.batch_size == self.batch_size
+        assert mapper.data_size == G.number_of_edges()
+        assert len(mapper.ids) == G.number_of_edges()
+
+        G = example_DiGraph_1(self.n_feat)
+        edge_labels = [0] * G.number_of_edges()
+        generator = Attri2VecLinkGenerator(G, batch_size=self.batch_size)
+        mapper = generator.flow(G.edges(), edge_labels)
+        assert generator.batch_size == self.batch_size
+        assert mapper.data_size == G.number_of_edges()
+        assert len(mapper.ids) == G.number_of_edges()
+
+    def test_Attri2VecLinkGenerator_1(self):
+
+        G = example_Graph_2(self.n_feat)
+        data_size = G.number_of_edges()
+        edge_labels = [0] * data_size
+
+        mapper = Attri2VecLinkGenerator(G, batch_size=self.batch_size).flow(
+            G.edges(), edge_labels
+        )
+
+        assert len(mapper) == 2
+
+        for batch in range(len(mapper)):
+            nf, nl = mapper[batch]
+            assert len(nf) == 2
+            assert nf[0].shape == (min(self.batch_size, data_size), self.n_feat)
+            assert nf[1].shape == (min(self.batch_size, data_size),)
+            assert len(nl) == min(self.batch_size, data_size)
+            assert all(nl == 0)
+
+        with pytest.raises(IndexError):
+            nf, nl = mapper[2]
+
+    def test_Attri2VecLinkGenerator_shuffle(self):
+        def test_edge_consistency(shuffle):
+            G = example_Graph_2(1)
+            edges = list(G.edges())
+            nodes = list(G.nodes())
+            edge_labels = list(range(len(edges)))
+
+            mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
+                edges, edge_labels, shuffle=shuffle
+            )
+
+            assert len(mapper) == 2
+
+            for batch in range(len(mapper)):
+                nf, nl = mapper[batch]
+                e1 = edges[nl[0]]
+                e2 = edges[nl[1]]
+                assert nf[0][0, 0] == e1[0]
+                assert nf[1][0] == nodes.index(e1[1])
+                assert nf[0][1, 0] == e2[0]
+                assert nf[1][1] == nodes.index(e2[1])
+
+        test_edge_consistency(True)
+        test_edge_consistency(False)
+
+    def test_Attri2VecLinkGenerator_not_Stellargraph(self):
+        G = nx.Graph()
+        elist = [(1, 2), (2, 3), (1, 4), (3, 2)]
+        G.add_edges_from(elist)
+
+        # Add example features
+        for v in G.nodes():
+            G.node[v]["feature"] = np.ones(1)
+
+        with pytest.raises(TypeError):
+            Attri2VecLinkGenerator(G, batch_size=self.batch_size)
+
+    def test_Attri2VecLinkGenerator_no_targets(self):
+        """
+        This tests link generator's iterator for prediction, i.e., without targets provided
+        """
+        G = example_Graph_2(self.n_feat)
+        gen = Attri2VecLinkGenerator(G, batch_size=self.batch_size).flow(G.edges())
+        for i in range(len(gen)):
+            assert gen[i][1] is None
+
+    def test_Attri2VecLinkGenerator_unsupervisedSampler_flow(self):
+        """
+        This tests link generator's initialization for on demand link generation i.e. there is no pregenerated list of samples provided to it.
+        """
+        n_feat = 4
+        n_batch = 2
+
+        # test graph
+        G = example_graph_random(
+            feature_size=n_feat, n_nodes=6, n_isolates=2, n_edges=10
+        )
+
+        unsupervisedSamples = UnsupervisedSampler(G, nodes=G.nodes)
+
+        gen = Attri2VecLinkGenerator(G, batch_size=n_batch).flow(unsupervisedSamples)
+
+        # The flow method is not passed UnsupervisedSampler object or a list of samples is not passed
+        with pytest.raises(TypeError):
+            gen = Attri2VecLinkGenerator(G, batch_size=n_batch).flow(
+                "not_a_list_of_samples_or_a_sample_generator"
+            )
+
+        # The flow method is not passed nothing
+        with pytest.raises(TypeError):
+            gen = Attri2VecLinkGenerator(G, batch_size=n_batch).flow()
+
+    def test_Attri2VecLinkGenerator_unsupervisedSampler_sample_generation(self):
+
+        G = example_Graph_2(self.n_feat)
+
+        unsupervisedSamples = UnsupervisedSampler(G)
+
+        mapper = Attri2VecLinkGenerator(G, batch_size=self.batch_size).flow(
+            unsupervisedSamples
+        )
+
+        assert mapper.data_size == 16
+        assert self.batch_size == 2
+        assert len(mapper) == 8
+
+        for batch in range(len(mapper)):
+            nf, nl = mapper[batch]
+
+            assert len(nf) == 2
+            assert len(set(mapper.head_node_types)) == 1
+
+            assert nf[0].shape == (min(self.batch_size, mapper.data_size), self.n_feat)
+            assert nf[1].shape == (min(self.batch_size, mapper.data_size),)
+            assert len(nl) == min(self.batch_size, mapper.data_size)
+            assert sorted(nl) == [0, 1]
+
+        with pytest.raises(IndexError):
+            nf, nl = mapper[8]

From 1a8a981891b07e966fc1fc93af887137025e4929 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:16:15 +1000
Subject: [PATCH 18/82] Add tests for Attri2Vec class

---
 tests/layer/test_attri2vec.py | 138 ++++++++++++++++++++++++++++++++++
 1 file changed, 138 insertions(+)
 create mode 100644 tests/layer/test_attri2vec.py

diff --git a/tests/layer/test_attri2vec.py b/tests/layer/test_attri2vec.py
new file mode 100644
index 000000000..84d188219
--- /dev/null
+++ b/tests/layer/test_attri2vec.py
@@ -0,0 +1,138 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2018 Data61, CSIRO
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+Attri2Vec tests
+
+"""
+from stellargraph.core.graph import StellarGraph
+from stellargraph.mapper.node_mappers import Attri2VecNodeGenerator
+from stellargraph.layer.attri2vec import *
+
+import keras
+import numpy as np
+import networkx as nx
+import pytest
+
+from keras.engine import saving
+
+
+def example_graph_1(feature_size=None):
+    G = nx.Graph()
+    elist = [(1, 2), (2, 3), (1, 4), (3, 2)]
+    G.add_nodes_from([1, 2, 3, 4], label="default")
+    G.add_edges_from(elist, label="default")
+
+    # Add example features
+    if feature_size is not None:
+        for v in G.nodes():
+            G.node[v]["feature"] = np.ones(feature_size)
+        return StellarGraph(G, node_features="feature")
+
+    else:
+        return StellarGraph(G)
+
+
+def test_attri2vec_constructor():
+    attri2vec = Attri2Vec(layer_sizes=[4], input_dim=2, node_num=4, normalize="l2")
+    assert attri2vec.dims == [2, 4]
+    assert attri2vec.input_node_num == 4
+    assert attri2vec.n_layers == 1
+    assert attri2vec.bias == False
+
+    # Check incorrect normalization flag
+    with pytest.raises(ValueError):
+        Attri2Vec(layer_sizes=[4], input_dim=2, node_num=4, normalize=lambda x: x)
+
+    with pytest.raises(ValueError):
+        Attri2Vec(layer_sizes=[4], input_dim=2, node_num=4, normalize="unknown")
+
+    # Check requirement for generator or input_dim and node_num
+    with pytest.raises(RuntimeError):
+        Attri2Vec(layer_sizes=[4])
+
+    # Construction from generator
+    G = example_graph_1(feature_size=3)
+    gen = Attri2VecNodeGenerator(G, batch_size=2).flow([1, 2])
+    attri2vec = Attri2Vec(layer_sizes=[4, 8], generator=gen, bias=True)
+
+    assert attri2vec.dims == [3, 4, 8]
+    assert attri2vec.input_node_num == 4
+    assert attri2vec.n_layers == 2
+    assert attri2vec.bias
+
+
+def test_attri2vec_apply():
+    attri2vec = Attri2Vec(
+        layer_sizes=[4], bias=False, input_dim=2, node_num=4, normalize=None
+    )
+
+    inp = keras.Input(shape=(1, 2))
+    out = attri2vec(inp)
+    model = keras.Model(inputs=inp, outputs=out)
+
+
+def test_attri2vec_apply_1():
+    attri2vec = Attri2Vec(
+        layer_sizes=[2, 2, 2], bias=False, input_dim=2, node_num=4, normalize=None
+    )
+    attri2vec.activation = "linear"
+    attri2vec.initializer = "ones"
+
+    inp = keras.Input(shape=(2,))
+    out = attri2vec(inp)
+    model = keras.Model(inputs=inp, outputs=out)
+
+    x = np.array([[1, 2]])
+    expected = np.array([[12, 12]])
+
+    actual = model.predict(x)
+    assert expected == pytest.approx(actual)
+
+    # Use the node model:
+    xinp, xout = attri2vec.node_model()
+    model2 = keras.Model(inputs=xinp, outputs=xout)
+    assert pytest.approx(expected) == model2.predict(x)
+
+
+def test_attri2vec_serialize():
+    attri2vec = Attri2Vec(
+        layer_sizes=[4], bias=False, input_dim=2, node_num=4, normalize=None
+    )
+    attri2vec.activation = "linear"
+    attri2vec.initializer = "ones"
+
+    inp = keras.Input(shape=(2,))
+    out = attri2vec(inp)
+    model = keras.Model(inputs=inp, outputs=out)
+
+    # Save model
+    model_json = model.to_json()
+
+    # Set all weights to one
+    model_weights = [np.ones_like(w) for w in model.get_weights()]
+
+    # Load model from json & set all weights
+    model2 = keras.models.model_from_json(model_json)
+    model2.set_weights(model_weights)
+
+    # Test loaded model
+    x = np.array([[1, 2]])
+    expected = np.array([[3, 3, 3, 3]])
+
+    actual = model2.predict(x)
+    assert expected == pytest.approx(actual)

From 4085790f643df36836be68240d3a0dafe93adc4a Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:17:46 +1000
Subject: [PATCH 19/82] Add the Attri2Vec, Attri2VecNodeGenerator and
 Attri2VecLinkGenerator to api

---
 docs/api.txt | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)
 mode change 100644 => 100755 docs/api.txt

diff --git a/docs/api.txt b/docs/api.txt
old mode 100644
new mode 100755
index c206b3efc..2479042df
--- a/docs/api.txt
+++ b/docs/api.txt
@@ -19,7 +19,7 @@ Generators
 -----------
 
 .. automodule:: stellargraph.mapper
-  :members: FullBatchNodeGenerator, GraphSAGENodeGenerator, GraphSAGELinkGenerator, HinSAGENodeGenerator, HinSAGELinkGenerator
+  :members: FullBatchNodeGenerator, GraphSAGENodeGenerator, GraphSAGELinkGenerator, HinSAGENodeGenerator, HinSAGELinkGenerator, Attri2VecNodeGenerator, Attri2VecLinkGenerator
 
 
 GraphSAGE model
@@ -36,6 +36,13 @@ HinSAGE model
   :members: HinSAGE, MeanHinAggregator
 
 
+Attri2Vec model
+----------------
+
+.. automodule:: stellargraph.layer.attri2vec
+  :members: Attri2Vec
+
+
 GCN model
 -------------
 

From 37ce44df007c65892a1ef0c453294937cf955325 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:20:18 +1000
Subject: [PATCH 20/82] Add attri2vec module to the
 stellargraph/layer/__init__.py file

---
 stellargraph/layer/__init__.py | 1 +
 1 file changed, 1 insertion(+)
 mode change 100644 => 100755 stellargraph/layer/__init__.py

diff --git a/stellargraph/layer/__init__.py b/stellargraph/layer/__init__.py
old mode 100644
new mode 100755
index 87926b106..228c7ce0b
--- a/stellargraph/layer/__init__.py
+++ b/stellargraph/layer/__init__.py
@@ -27,5 +27,6 @@
 from .graph_attention import *
 from .link_inference import *
 from .gcn import *
+from .attri2vec import *
 from .misc import SqueezedSparseConversion
 from .preprocessing_layer import GraphPreProcessingLayer

From 208835e60368a2908d32ef23f9e3fd04aeab2a43 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 21 Aug 2019 17:33:54 +1000
Subject: [PATCH 21/82] rerun the attri2vec demo

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 26 +++++++++----------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
index 47aefb365..52bd0b6ba 100644
--- a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
+++ b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
@@ -456,7 +456,7 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0821 16:57:15.264071 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "W0821 17:24:39.472835 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
       "\n"
      ]
     }
@@ -477,9 +477,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0821 16:57:18.045172 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "W0821 17:24:40.300625 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
       "\n",
-      "W0821 16:57:18.051191 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "W0821 17:24:40.306243 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
       "\n"
      ]
     }
@@ -531,11 +531,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0821 16:57:23.894735 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "W0821 17:24:43.567921 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
       "\n",
-      "W0821 16:57:23.901146 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+      "W0821 17:24:43.574287 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
       "\n",
-      "W0821 16:57:23.909626 140736102417344 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0821 17:24:43.582134 140736102417344 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -569,7 +569,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0821 16:57:27.992636 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
+      "W0821 17:24:45.546332 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
       "\n"
      ]
     },
@@ -578,13 +578,13 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/4\n",
-      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.6506 - binary_accuracy: 0.6515\n",
+      "4711/4711 [==============================] - 92s 20ms/step - loss: 0.6903 - binary_accuracy: 0.6251\n",
       "Epoch 2/4\n",
-      "4711/4711 [==============================] - 94s 20ms/step - loss: 0.5718 - binary_accuracy: 0.6775\n",
+      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.6135 - binary_accuracy: 0.6346\n",
       "Epoch 3/4\n",
-      "4711/4711 [==============================] - 95s 20ms/step - loss: 0.4567 - binary_accuracy: 0.7736\n",
+      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.5289 - binary_accuracy: 0.7100\n",
       "Epoch 4/4\n",
-      "4711/4711 [==============================] - 94s 20ms/step - loss: 0.2897 - binary_accuracy: 0.8791\n"
+      "4711/4711 [==============================] - 94s 20ms/step - loss: 0.3581 - binary_accuracy: 0.8372\n"
      ]
     }
    ],
@@ -784,7 +784,7 @@
     {
      "data": {
       "text/plain": [
-       "0.8109590038749268"
+       "0.7857757735930484"
       ]
      },
      "execution_count": 32,
@@ -895,7 +895,7 @@
     {
      "data": {
       "text/plain": [
-       "0.6638026335702683"
+       "0.6631950060001668"
       ]
      },
      "execution_count": 37,

From 6179a3adc08bffe76ec74074b0b02c828d637940 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Thu, 22 Aug 2019 10:52:27 +1000
Subject: [PATCH 22/82] update the attri2vec demo by fixing some typos

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 33 +++++++++----------
 1 file changed, 16 insertions(+), 17 deletions(-)

diff --git a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
index 52bd0b6ba..618865352 100644
--- a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
+++ b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
@@ -56,8 +56,7 @@
     "import stellargraph as sg\n",
     "from stellargraph.data import UniformRandomWalk\n",
     "from stellargraph.data import UnsupervisedSampler\n",
-    "from stellargraph.mapper import Attri2VecLinkGenerator\n",
-    "from stellargraph.mapper.node_mappers import Attri2VecNodeGenerator\n",
+    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
     "from stellargraph.layer import Attri2Vec, link_classification\n",
     "\n",
     "import keras\n",
@@ -456,7 +455,7 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0821 17:24:39.472835 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "W0822 10:43:54.482249 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
       "\n"
      ]
     }
@@ -477,9 +476,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0821 17:24:40.300625 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "W0822 10:43:56.449973 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
       "\n",
-      "W0821 17:24:40.306243 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "W0822 10:43:56.455691 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
       "\n"
      ]
     }
@@ -531,11 +530,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0821 17:24:43.567921 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "W0822 10:44:02.083713 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
       "\n",
-      "W0821 17:24:43.574287 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+      "W0822 10:44:02.091721 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
       "\n",
-      "W0821 17:24:43.582134 140736102417344 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0822 10:44:02.102672 140736102417344 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -569,7 +568,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0821 17:24:45.546332 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
+      "W0822 10:44:04.704549 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
       "\n"
      ]
     },
@@ -578,13 +577,13 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/4\n",
-      "4711/4711 [==============================] - 92s 20ms/step - loss: 0.6903 - binary_accuracy: 0.6251\n",
+      "4711/4711 [==============================] - 89s 19ms/step - loss: 0.6678 - binary_accuracy: 0.6432\n",
       "Epoch 2/4\n",
-      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.6135 - binary_accuracy: 0.6346\n",
+      "4711/4711 [==============================] - 92s 19ms/step - loss: 0.5859 - binary_accuracy: 0.6642\n",
       "Epoch 3/4\n",
-      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.5289 - binary_accuracy: 0.7100\n",
+      "4711/4711 [==============================] - 91s 19ms/step - loss: 0.4810 - binary_accuracy: 0.7541\n",
       "Epoch 4/4\n",
-      "4711/4711 [==============================] - 94s 20ms/step - loss: 0.3581 - binary_accuracy: 0.8372\n"
+      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.3283 - binary_accuracy: 0.8544\n"
      ]
     }
    ],
@@ -676,11 +675,11 @@
     "    if source_year < year_thresh and target_year < year_thresh:\n",
     "        in_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
     "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
-    "        in_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative node\n",
+    "        in_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative edge\n",
     "    else:\n",
     "        out_of_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
     "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
-    "        out_of_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative node\n",
+    "        out_of_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative edge\n",
     "in_sample_edges = np.array(in_sample_edges)\n",
     "out_of_sample_edges = np.array(out_of_sample_edges)"
    ]
@@ -784,7 +783,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7857757735930484"
+       "0.798202033278801"
       ]
      },
      "execution_count": 32,
@@ -895,7 +894,7 @@
     {
      "data": {
       "text/plain": [
-       "0.6631950060001668"
+       "0.6638897794650095"
       ]
      },
      "execution_count": 37,

From f959e65469f05ab8e620be732ae7f0d56c266ae1 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 10:57:25 +1000
Subject: [PATCH 23/82] code update

---
 tests/mapper/test_link_mappers.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/mapper/test_link_mappers.py b/tests/mapper/test_link_mappers.py
index b4a428f11..8442cd088 100755
--- a/tests/mapper/test_link_mappers.py
+++ b/tests/mapper/test_link_mappers.py
@@ -837,7 +837,6 @@ def test_Attri2VecLinkGenerator_unsupervisedSampler_sample_generation(self):
             nf, nl = mapper[batch]
 
             assert len(nf) == 2
-            assert len(set(mapper.head_node_types)) == 1
 
             assert nf[0].shape == (min(self.batch_size, mapper.data_size), self.n_feat)
             assert nf[1].shape == (min(self.batch_size, mapper.data_size),)

From 36a7cd8b2ca384e153687a1ce0298ae527ebc466 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 10:57:46 +1000
Subject: [PATCH 24/82] code update

---
 tests/layer/test_attri2vec.py | 68 ++++++++++++++++++++++-------------
 1 file changed, 44 insertions(+), 24 deletions(-)

diff --git a/tests/layer/test_attri2vec.py b/tests/layer/test_attri2vec.py
index 84d188219..0701128a6 100644
--- a/tests/layer/test_attri2vec.py
+++ b/tests/layer/test_attri2vec.py
@@ -23,13 +23,11 @@
 from stellargraph.mapper.node_mappers import Attri2VecNodeGenerator
 from stellargraph.layer.attri2vec import *
 
-import keras
+from tensorflow import keras
 import numpy as np
 import networkx as nx
 import pytest
 
-from keras.engine import saving
-
 
 def example_graph_1(feature_size=None):
     G = nx.Graph()
@@ -78,43 +76,65 @@ def test_attri2vec_constructor():
 
 def test_attri2vec_apply():
     attri2vec = Attri2Vec(
-        layer_sizes=[4], bias=False, input_dim=2, node_num=4, normalize=None
-    )
-
-    inp = keras.Input(shape=(1, 2))
-    out = attri2vec(inp)
-    model = keras.Model(inputs=inp, outputs=out)
-
-
-def test_attri2vec_apply_1():
-    attri2vec = Attri2Vec(
-        layer_sizes=[2, 2, 2], bias=False, input_dim=2, node_num=4, normalize=None
+        layer_sizes=[2, 2, 2],
+        bias=False,
+        input_dim=2,
+        node_num=4,
+        activation="linear",
+        normalize=None,
     )
-    attri2vec.activation = "linear"
-    attri2vec.initializer = "ones"
-
-    inp = keras.Input(shape=(2,))
-    out = attri2vec(inp)
-    model = keras.Model(inputs=inp, outputs=out)
 
     x = np.array([[1, 2]])
     expected = np.array([[12, 12]])
 
-    actual = model.predict(x)
+    inp = keras.Input(shape=(2,))
+    out = attri2vec(inp)
+    model1 = keras.Model(inputs=inp, outputs=out)
+    model_weights1 = [np.ones_like(w) for w in model1.get_weights()]
+    model1.set_weights(model_weights1)
+    actual = model1.predict(x)
     assert expected == pytest.approx(actual)
 
     # Use the node model:
     xinp, xout = attri2vec.node_model()
     model2 = keras.Model(inputs=xinp, outputs=xout)
+    model_weights2 = [np.ones_like(w) for w in model2.get_weights()]
+    model2.set_weights(model_weights2)
     assert pytest.approx(expected) == model2.predict(x)
 
+    x1 = np.array([[3, 1]])
+    x2 = np.array([[2]])
+    y1 = np.array([[16, 16]])
+    y2 = np.array([[1, 1]])
+
+    # Test the build function:
+    xinp, xout = attri2vec.build()
+    model3 = keras.Model(inputs=xinp, outputs=xout)
+    model_weights3 = [np.ones_like(w) for w in model3.get_weights()]
+    model3.set_weights(model_weights3)
+    actual = model3.predict([x1, x2])
+    assert pytest.approx(y1) == actual[0]
+    assert pytest.approx(y2) == actual[1]
+
+    # Use the link model:
+    xinp, xout = attri2vec.link_model()
+    model4 = keras.Model(inputs=xinp, outputs=xout)
+    model_weights4 = [np.ones_like(w) for w in model4.get_weights()]
+    model4.set_weights(model_weights4)
+    actual = model4.predict([x1, x2])
+    assert pytest.approx(y1) == actual[0]
+    assert pytest.approx(y2) == actual[1]
+
 
 def test_attri2vec_serialize():
     attri2vec = Attri2Vec(
-        layer_sizes=[4], bias=False, input_dim=2, node_num=4, normalize=None
+        layer_sizes=[4],
+        bias=False,
+        input_dim=2,
+        node_num=4,
+        activation="linear",
+        normalize=None,
     )
-    attri2vec.activation = "linear"
-    attri2vec.initializer = "ones"
 
     inp = keras.Input(shape=(2,))
     out = attri2vec(inp)

From a50390f943170b5d5b5014985d3e507bc4cae777 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 10:58:46 +1000
Subject: [PATCH 25/82] add attri2vec link prediction demo

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 933 ++++++++++++++++++
 1 file changed, 933 insertions(+)
 create mode 100644 demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb

diff --git a/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb b/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
new file mode 100644
index 000000000..984cbb562
--- /dev/null
+++ b/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
@@ -0,0 +1,933 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inductive Node Representation Learning through attri2vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
+    "\n",
+    "## attri2vec\n",
+    "\n",
+    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
+    "\n",
+    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes.\n",
+    "\n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "\n",
+    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model.\n",
+    "\n",
+    "In this demo, we first train the attri2vec model on the in-sample subgraph and obtain a mapping function from node attributes to node representations, then apply the mapping function to the content attributes of out-of-sample nodes and obtain the representations of out-of-sample nodes. We evaluate the quality of inferred out-of-sample node representations by using it to predict the links of out-of-sample nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import stellargraph as sg\n",
+    "from stellargraph.data import UnsupervisedSampler\n",
+    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
+    "from stellargraph.layer import Attri2Vec, link_classification\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import roc_auc_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading DBLP network data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This demo uses a DBLP citation network, a subgraph extracted from [DBLP-Citation-network V3](https://aminer.org/citation). To form this subgraph, papers from four subjects are extracted according to their venue information: *Database, Data Mining, Artificial Intelligence and Computer Vision*, and papers with no citations are removed. The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word. By ignoring the citation direction, we take the DBLP subgraph as an undirected network.\n",
+    "\n",
+    "As papers in DBLP are attached with publication year, the DBLP network with the dynamic property can be used to study the problem of out-of-sample node representation learning. From the DBLP network, we construct four in-sample subgraphs using papers published before 2006, 2007, 2008 and 2009, and denote the four subgraphs as DBLP2006, DBLP2007, DBLP2008, and DBLP2009. For each subgraph, the remaining papers are taken as out-of-sample nodes. We consider the case where new coming nodes have no links. We predict the links of out-of-sample nodes using the learned out-of-sample node representations and compare its performance with the node content feature baseline.\n",
+    "\n",
+    "The dataset used in this demo can be downloaded from https://www.kaggle.com/daozhang/dblp-subgraph.\n",
+    "The following is the description of the dataset:\n",
+    "\n",
+    "> The content.txt file contains descriptions of the papers in the following format:\n",
+    "\n",
+    " \t\t<paper_id> <word_attributes> <class_label> <publication_year>\n",
+    "        \n",
+    "> The first entry in each line contains the unique integer ID (ranging from 0 to 18,447) of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last two entries in the line are the class label and the publication year of the paper.\n",
+    "> The edgeList.txt file contains the citation relations. Each line describes a link in the following format:\n",
+    "\t\t\n",
+    "        <ID of paper1> <ID of paper2>\n",
+    "        \n",
+    "> Each line contains two paper IDs, with paper2 citing paper1 or paper1 citing paper2.\n",
+    "\n",
+    "\n",
+    "Download and unzip the dblp-subgraph.zip file to a location on your computer and set the `data_dir` variable to\n",
+    "point to the location of the dataset (the \"DBLP\" directory containing \"content.txt\" and \"edgeList.txt\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"~/data/DBLP\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the graph from the edgelist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edgelist = pd.read_csv(os.path.join(data_dir, \"edgeList.txt\"), sep='\\t', header=None, names=[\"source\", \"target\"])\n",
+    "edgelist[\"label\"] = \"cites\"  # set the edge type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load paper content features, subjects and publishing years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_names = [\"w_{}\".format(ii) for ii in range(2476)]\n",
+    "node_column_names =  feature_names + [\"subject\", \"year\"]\n",
+    "node_data = pd.read_csv(os.path.join(data_dir, \"content.txt\"), sep='\\t', header=None, names=node_column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct the whole graph from edge list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_all_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(G_all_nx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_node_features = node_data[feature_names]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_all = sg.StellarGraph(G_all_nx, node_features=all_node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 18448, Edges: 45611\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [18448]\n",
+      "    Edge types: paper-cites->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-cites->paper: [45611]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G_all.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get DBLP Subgraph \n",
+    "### with papers published before a threshold year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the edge list connecting in-sample nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year_thresh = 2006 # the threshold year for in-sample and out-of-sample set split, which can be 2007, 2008 and 2009\n",
+    "subgraph_edgelist = []\n",
+    "for ii in range(len(edgelist)):\n",
+    "    source_index = edgelist[\"source\"][ii]\n",
+    "    target_index = edgelist[\"target\"][ii]\n",
+    "    source_year = int(node_data[\"year\"][source_index])\n",
+    "    target_year = int(node_data[\"year\"][target_index])\n",
+    "    if source_year < year_thresh and target_year < year_thresh:\n",
+    "        subgraph_edgelist.append([source_index, target_index])\n",
+    "subgraph_edgelist = pd.DataFrame(np.array(subgraph_edgelist), columns=[\"source\", \"target\"])\n",
+    "subgraph_edgelist[\"label\"] = \"cites\" # set the edge type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct the network from the selected edge list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_sub_nx = nx.from_pandas_edgelist(subgraph_edgelist, edge_attr=\"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(G_sub_nx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the ids of the nodes in the selected subgraph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subgraph_node_ids = sorted(list(G_sub_nx.nodes))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node features of the selected subgraph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subgraph_node_features = node_data[feature_names].reindex(subgraph_node_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_sub = sg.StellarGraph(G_sub_nx, node_features=subgraph_node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 11776, Edges: 28937\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [11776]\n",
+      "    Edge types: paper-cites->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-cites->paper: [28937]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G_sub.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train attri2vec on the DBLP Subgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = list(G_sub.nodes())\n",
+    "number_of_walks = 2\n",
+    "length = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unsupervised_samples = UnsupervisedSampler(G_sub, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the batch size and the number of epochs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 50\n",
+    "epochs = 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Attri2VecLinkGenerator with an estimated 4710 batches generated on the fly per epoch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_gen = Attri2VecLinkGenerator(G_sub, batch_size).flow(unsupervised_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
+    "\n",
+    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term, no dropout and no normalization. (Dropout can be switched on by specifying a positive dropout rate, 0 < dropout < 1 and normalization can be set to 'l2'). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer_sizes = [128]\n",
+    "attri2vec = Attri2Vec(\n",
+    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0925 10:21:27.091693 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+      "W0925 10:21:27.113364 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
+    "x_inp, x_out = attri2vec.build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = link_classification(\n",
+    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
+    ")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
+    "    loss=keras.losses.binary_crossentropy,\n",
+    "    metrics=[keras.metrics.binary_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0925 10:21:27.574651 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/6\n",
+      "4711/4711 [==============================] - 72s 15ms/step - loss: 0.7680 - binary_accuracy: 0.5814\n",
+      "Epoch 2/6\n",
+      "4711/4711 [==============================] - 72s 15ms/step - loss: 0.6447 - binary_accuracy: 0.6101\n",
+      "Epoch 3/6\n",
+      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.5866 - binary_accuracy: 0.6483\n",
+      "Epoch 4/6\n",
+      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.4925 - binary_accuracy: 0.7284\n",
+      "Epoch 5/6\n",
+      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.3574 - binary_accuracy: 0.8300\n",
+      "Epoch 6/6\n",
+      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.2242 - binary_accuracy: 0.9105\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit_generator(\n",
+    "    train_gen,\n",
+    "    epochs=epochs,\n",
+    "    verbose=1,\n",
+    "    use_multiprocessing=True,\n",
+    "    workers=4,\n",
+    "    shuffle=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predicting links of out-of-sample nodes with the learned attri2vec model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inp_src = x_inp[0]\n",
+    "x_out_src = x_out[0]\n",
+    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node embeddings, for both in-sample and out-of-sample nodes, by applying the learned mapping function to node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "369/369 [==============================] - 1s 1ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_ids = node_data.index\n",
+    "node_gen = Attri2VecNodeGenerator(G_all, batch_size).flow(node_ids)\n",
+    "node_embeddings = embedding_model.predict_generator(node_gen, workers=4, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the positive and negative edges for in-sample nodes and out-of-sample nodes. The edges of the in-sample nodes only include the edges between in-sample nodes, and the edges of out-of-sample nodes are referred to all the edges linked to out-of-sample nodes, including the edges connecting in-sample and out-of-sample edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year_thresh = 2006\n",
+    "in_sample_edges = []\n",
+    "out_of_sample_edges = []\n",
+    "for ii in range(len(edgelist)):\n",
+    "    source_index = edgelist[\"source\"][ii]\n",
+    "    target_index = edgelist[\"target\"][ii]\n",
+    "    if source_index > target_index: # neglect edge direction for the undirected graph\n",
+    "        continue\n",
+    "    source_year = int(node_data[\"year\"][source_index])\n",
+    "    target_year = int(node_data[\"year\"][target_index])\n",
+    "    if source_year < year_thresh and target_year < year_thresh:\n",
+    "        in_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
+    "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
+    "        in_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative edge\n",
+    "    else:\n",
+    "        out_of_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
+    "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
+    "        out_of_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative edge\n",
+    "in_sample_edges = np.array(in_sample_edges)\n",
+    "out_of_sample_edges = np.array(out_of_sample_edges)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct the edge features from the learned node representations with l2 normed difference, where edge features are the element-wise square of the difference between the embeddings of two head nodes. Other strategy like element-wise product can also be used to construct edge features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_sample_edge_feat_from_emb = (node_embeddings[in_sample_edges[:,0]]-node_embeddings[in_sample_edges[:,1]])**2\n",
+    "out_of_sample_edge_feat_from_emb = (node_embeddings[out_of_sample_edges[:,0]]-node_embeddings[out_of_sample_edges[:,1]])**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the Logistic Regression classifier from in-sample edges with the edge features constructed from attri2vec embeddings. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
+       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf_edge_pred_from_emb = LogisticRegression(verbose=0, solver='lbfgs', multi_class=\"auto\", max_iter=500)\n",
+    "clf_edge_pred_from_emb.fit(in_sample_edge_feat_from_emb, in_sample_edges[:,2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict the edge existence probability with the trained Logistic Regression classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_pred_from_emb = clf_edge_pred_from_emb.predict_proba(out_of_sample_edge_feat_from_emb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the positive class index of `edge_pred_from_emb`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if clf_edge_pred_from_emb.classes_[0] == 1:\n",
+    "    positive_class_index = 0\n",
+    "else:\n",
+    "    positive_class_index = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the AUC score for the prediction with attri2vec embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7842243817182476"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(out_of_sample_edges[:,2], edge_pred_from_emb[:,positive_class_index])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the baseline, we also investigate the performance of node content features in predicting the edges of out-of-sample nodes. Firstly, we construct edge features from node content features with the same strategy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_sample_edge_rep_from_feat = (node_data[feature_names].values[in_sample_edges[:,0]]-node_data[feature_names].values[in_sample_edges[:,1]])**2\n",
+    "out_of_sample_edge_rep_from_feat = (node_data[feature_names].values[out_of_sample_edges[:,0]]-node_data[feature_names].values[out_of_sample_edges[:,1]])**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we train the Logistic Regression classifier from in-sample edges with the edge features constructed from node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
+       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf_edge_pred_from_feat = LogisticRegression(verbose=0, solver='lbfgs', multi_class=\"auto\", max_iter=500)\n",
+    "clf_edge_pred_from_feat.fit(in_sample_edge_rep_from_feat, in_sample_edges[:,2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict the edge existence probability with the trained Logistic Regression classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_pred_from_feat = clf_edge_pred_from_feat.predict_proba(out_of_sample_edge_rep_from_feat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get positive class index of `clf_edge_pred_from_feat`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if clf_edge_pred_from_feat.classes_[0] == 1:\n",
+    "    positive_class_index = 0\n",
+    "else:\n",
+    "    positive_class_index = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the AUC score for the prediction with node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6666971120688185"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(out_of_sample_edges[:,2], edge_pred_from_feat[:,positive_class_index])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "attri2vec can inductively infer the representations of out-of-sample nodes from their content attributes. As the inferred node representations well capture both structure and node content information, they perform much better than node content features in predicting the links of out-of-sample nodes."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 181c75e7f57abac9955145091222e081764a3438 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 10:59:42 +1000
Subject: [PATCH 26/82] add attri2vec DBLP embeddings demo

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 541 ++++--------------
 1 file changed, 103 insertions(+), 438 deletions(-)

diff --git a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
index 618865352..5b9148316 100644
--- a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
+++ b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Inferring Representations for Out-of-sample Nodes Through attri2vec"
+    "# Node Representation Learning with attri2vec on DBLP"
    ]
   },
   {
@@ -13,24 +13,16 @@
    "source": [
     "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
     "\n",
-    "## Dataset\n",
-    "This demo uses a DBLP citation network, a subgraph extracted from [DBLP-Citation-network V3](https://aminer.org/citation). To form this subgraph, papers from four subjects are extracted according to their venue information: *Database, Data Mining, Artificial Intelligence and Computer Vision*, and papers with no citations are removed. The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word. By ignoring the citation direction, we take the DBLP subgraph as an undirected network.\n",
-    "\n",
-    "As papers in DBLP are attached with publication year, the DBLP network with the dynamic property can be used to study the problem of out-of-sample node representation learning. From the DBLP network, we construct four in-sample subgraphs using papers published before 2006, 2007, 2008 and 2009, and denote the four subgraphs as DBLP2006, DBLP2007, DBLP2008, and DBLP2009. For each subgraph, the remaining papers are taken as out-of-sample nodes. We consider the case where new coming nodes have no links. We predict the links of out-of-sample nodes using the learned out-of-sample node representations and compare its performance with the node content feature baseline.\n",
-    "\n",
-    "\n",
     "\n",
     "## attri2vec\n",
     "\n",
-    "For networks attached with node content attributes, attri2vec infers node representations by discovering a latent node attribute subspace that respects network structure in a more consistent way. To transform network nodes from the original attribute space into the targeted subspace, a non-linear mapping is used. To make the mapped images respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace. Following [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec), attri2vec learns node representations by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
-    "\n",
-    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. Unlike the embeddings learned by DeepWalk/node2vec, the mapping is inductive. Different from the mapping generated by GraphSAGE, the mapping only performs on node content attributes and does not rely on any link information, which makes it possible to construct representations for new coming nodes having few or no link information from their content attributes.\n",
+    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
     "\n",
-    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
     "\n",
-    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model.\n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
     "\n",
-    "In this demo, we first train the attri2vec model on the in-sample subgraph and obtain a mapping function from node attributes to node representations, then apply the mapping function to the content attributes of out-of-sample nodes and obtain the representations of out-of-sample nodes. We evaluate the quality of inferred out-of-sample node representations by using it to predict the links of out-of-sample nodes."
+    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
    ]
   },
   {
@@ -54,15 +46,19 @@
     "import random\n",
     "\n",
     "import stellargraph as sg\n",
-    "from stellargraph.data import UniformRandomWalk\n",
     "from stellargraph.data import UnsupervisedSampler\n",
     "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
     "from stellargraph.layer import Attri2Vec, link_classification\n",
     "\n",
-    "import keras\n",
+    "from tensorflow import keras\n",
     "\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import roc_auc_score"
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.metrics import accuracy_score"
    ]
   },
   {
@@ -76,6 +72,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "This demo uses a DBLP citation network, a subgraph extracted from [DBLP-Citation-network V3](https://aminer.org/citation). To form this subgraph, papers from four subjects are extracted according to their venue information: *Database, Data Mining, Artificial Intelligence and Computer Vision*, and papers with no citations are removed. The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word. By ignoring the citation direction, we take the DBLP subgraph as an undirected network.\n",
+    "\n",
     "The dataset used in this demo can be downloaded from https://www.kaggle.com/daozhang/dblp-subgraph.\n",
     "The following is the description of the dataset:\n",
     "\n",
@@ -143,7 +141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Construct the whole graph from edge list."
+    "Construct the graph from edge list."
    ]
   },
   {
@@ -152,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "G_all_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
+    "G_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
    ]
   },
   {
@@ -168,7 +166,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nx.set_node_attributes(G_all_nx, \"paper\", \"label\")"
+    "nx.set_node_attributes(G_nx, \"paper\", \"label\")"
    ]
   },
   {
@@ -184,7 +182,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_node_features = node_data[feature_names]"
+    "node_features = node_data[feature_names]"
    ]
   },
   {
@@ -200,7 +198,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "G_all = sg.StellarGraph(G_all_nx, node_features=all_node_features)"
+    "G = sg.StellarGraph(G_nx, node_features=node_features)"
    ]
   },
   {
@@ -226,132 +224,7 @@
     }
    ],
    "source": [
-    "print(G_all.info())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get DBLP Subgraph \n",
-    "### with papers published before a threshold year"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the edge list connecting in-sample nodes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "year_thresh = 2006 # the threshold year for in-sample and out-of-sample set split, which can be 2007, 2008 and 2009\n",
-    "subgraph_edgelist = []\n",
-    "for ii in range(len(edgelist)):\n",
-    "    source_index = edgelist[\"source\"][ii]\n",
-    "    target_index = edgelist[\"target\"][ii]\n",
-    "    source_year = int(node_data[\"year\"][source_index])\n",
-    "    target_year = int(node_data[\"year\"][target_index])\n",
-    "    if source_year < year_thresh and target_year < year_thresh:\n",
-    "        subgraph_edgelist.append([source_index, target_index])\n",
-    "subgraph_edgelist = pd.DataFrame(np.array(subgraph_edgelist), columns=[\"source\", \"target\"])\n",
-    "subgraph_edgelist[\"label\"] = \"cites\" # set the edge type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Construct the network from the selected edge list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G_sub_nx = nx.from_pandas_edgelist(subgraph_edgelist, edge_attr=\"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify node types."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nx.set_node_attributes(G_sub_nx, \"paper\", \"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get in-sample node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subgraph_node_data = node_data[node_data[\"year\"]<2006]\n",
-    "subgraph_node_features = subgraph_node_data[feature_names]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the Stellargraph with node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G_sub = sg.StellarGraph(G_sub_nx, node_features=subgraph_node_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 11776, Edges: 28937\n",
-      "\n",
-      " Node types:\n",
-      "  paper: [11776]\n",
-      "    Edge types: paper-cites->paper\n",
-      "\n",
-      " Edge types:\n",
-      "    paper-cites->paper: [28937]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(G_sub.info())"
+    "print(G.info())"
    ]
   },
   {
@@ -365,17 +238,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk, and random seed."
+    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "nodes = list(G_sub.nodes())\n",
-    "number_of_walks = 2\n",
+    "nodes = list(G.nodes())\n",
+    "number_of_walks = 4\n",
     "length = 5"
    ]
   },
@@ -388,11 +261,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "unsupervised_samples = UnsupervisedSampler(G_sub, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
    ]
   },
   {
@@ -404,12 +277,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "batch_size = 50\n",
-    "epochs = 4"
+    "epochs = 8"
    ]
   },
   {
@@ -421,19 +294,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running Attri2VecLinkGenerator with an estimated 4710 batches generated on the fly per epoch.\n"
+      "Running Attri2VecLinkGenerator with an estimated 14758 batches generated on the fly per epoch.\n"
      ]
     }
    ],
    "source": [
-    "train_gen = Attri2VecLinkGenerator(G_sub, batch_size).flow(unsupervised_samples)"
+    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
    ]
   },
   {
@@ -442,44 +315,37 @@
    "source": [
     "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
     "\n",
-    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term, no dropout and no normalization. (Dropout can be switched on by specifying a positive dropout rate, 0 < dropout < 1 and normalization can be set to 'l2'). "
+    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0822 10:43:54.482249 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "layer_sizes = [128]\n",
     "attri2vec = Attri2Vec(\n",
-    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, dropout=0.0, normalize=\"None\"\n",
+    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0822 10:43:56.449973 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
-      "\n",
-      "W0822 10:43:56.455691 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
-      "\n"
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0924 14:51:32.580700 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+      "W0924 14:51:32.618021 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
     }
    ],
@@ -497,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -523,23 +389,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "W0822 10:44:02.083713 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
-      "\n",
-      "W0822 10:44:02.091721 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
-      "\n",
-      "W0822 10:44:02.102672 140736102417344 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
     "\n",
@@ -559,31 +411,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0822 10:44:04.704549 140736102417344 deprecation_wrapper.py:119] From /anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
-      "\n"
+      "W0924 14:51:33.217601 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/4\n",
-      "4711/4711 [==============================] - 89s 19ms/step - loss: 0.6678 - binary_accuracy: 0.6432\n",
-      "Epoch 2/4\n",
-      "4711/4711 [==============================] - 92s 19ms/step - loss: 0.5859 - binary_accuracy: 0.6642\n",
-      "Epoch 3/4\n",
-      "4711/4711 [==============================] - 91s 19ms/step - loss: 0.4810 - binary_accuracy: 0.7541\n",
-      "Epoch 4/4\n",
-      "4711/4711 [==============================] - 93s 20ms/step - loss: 0.3283 - binary_accuracy: 0.8544\n"
+      "Epoch 1/8\n",
+      "14759/14759 [==============================] - 384s 26ms/step - loss: 0.7016 - binary_accuracy: 0.5404\n",
+      "Epoch 2/8\n",
+      "14759/14759 [==============================] - 397s 27ms/step - loss: 0.6156 - binary_accuracy: 0.6309\n",
+      "Epoch 3/8\n",
+      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.5221 - binary_accuracy: 0.7362\n",
+      "Epoch 4/8\n",
+      "14759/14759 [==============================] - 407s 28ms/step - loss: 0.4527 - binary_accuracy: 0.7909\n",
+      "Epoch 5/8\n",
+      "14759/14759 [==============================] - 389s 26ms/step - loss: 0.4029 - binary_accuracy: 0.8219\n",
+      "Epoch 6/8\n",
+      "14759/14759 [==============================] - 409s 28ms/step - loss: 0.3673 - binary_accuracy: 0.8424\n",
+      "Epoch 7/8\n",
+      "14759/14759 [==============================] - 399s 27ms/step - loss: 0.3387 - binary_accuracy: 0.8573\n",
+      "Epoch 8/8\n",
+      "14759/14759 [==============================] - 387s 26ms/step - loss: 0.3189 - binary_accuracy: 0.8679\n"
      ]
     }
    ],
@@ -592,8 +453,8 @@
     "    train_gen,\n",
     "    epochs=epochs,\n",
     "    verbose=1,\n",
-    "    use_multiprocessing=True,\n",
-    "    workers=4,\n",
+    "    use_multiprocessing=False,\n",
+    "    workers=1,\n",
     "    shuffle=True,\n",
     ")"
    ]
@@ -602,7 +463,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Predicting links of out-of-sample nodes with the learned attri2vec model"
+    "##  Visualise Node Embeddings"
    ]
   },
   {
@@ -614,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,12 +488,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Get the node embeddings, for both in-sample and out-of-sample nodes, by applying the learned mapping function to node content features."
+    "Get the node embeddings by applying the learned mapping function to node content features."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -645,272 +506,76 @@
    ],
    "source": [
     "node_ids = node_data.index\n",
-    "node_gen = Attri2VecNodeGenerator(G_all, batch_size).flow(node_ids)\n",
-    "node_embeddings = embedding_model.predict_generator(node_gen, workers=4, verbose=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the positive and negative edges for in-sample nodes and out-of-sample nodes. The edges of the in-sample nodes only include the edges between in-sample nodes, and the edges of out-of-sample nodes are referred to all the edges linked to out-of-sample nodes, including the edges connecting in-sample and out-of-sample edges."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "year_thresh = 2006\n",
-    "in_sample_edges = []\n",
-    "out_of_sample_edges = []\n",
-    "for ii in range(len(edgelist)):\n",
-    "    source_index = edgelist[\"source\"][ii]\n",
-    "    target_index = edgelist[\"target\"][ii]\n",
-    "    if source_index > target_index: # neglect edge direction for the undirected graph\n",
-    "        continue\n",
-    "    source_year = int(node_data[\"year\"][source_index])\n",
-    "    target_year = int(node_data[\"year\"][target_index])\n",
-    "    if source_year < year_thresh and target_year < year_thresh:\n",
-    "        in_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
-    "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
-    "        in_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative edge\n",
-    "    else:\n",
-    "        out_of_sample_edges.append([source_index, target_index, 1]) # get the positive edge\n",
-    "        negative_target_index = unsupervised_samples.random.choices(node_data.index.tolist(), k=1) # generate negative node\n",
-    "        out_of_sample_edges.append([source_index, negative_target_index[0], 0]) # get the negative edge\n",
-    "in_sample_edges = np.array(in_sample_edges)\n",
-    "out_of_sample_edges = np.array(out_of_sample_edges)"
+    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
+    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Construct the edge features from the learned node representations with l2 normed difference, where edge features are the element-wise square of the difference between the embeddings of two head nodes. Other strategy like element-wise product can also be used to construct edge features."
+    "Get node subjects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "in_sample_edge_feat_from_emb = (node_embeddings[in_sample_edges[:,0]]-node_embeddings[in_sample_edges[:,1]])**2\n",
-    "out_of_sample_edge_feat_from_emb = (node_embeddings[out_of_sample_edges[:,0]]-node_embeddings[out_of_sample_edges[:,1]])**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Train the Logistic Regression classifier from in-sample edges with the edge features constructed from attri2vec embeddings. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
-       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clf_edge_pred_from_emb = LogisticRegression(verbose=0, solver='lbfgs', multi_class=\"auto\", max_iter=500)\n",
-    "clf_edge_pred_from_emb.fit(in_sample_edge_feat_from_emb, in_sample_edges[:,2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Predict the edge existence probability with the trained Logistic Regression classifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edge_pred_from_emb = clf_edge_pred_from_emb.predict_proba(out_of_sample_edge_feat_from_emb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the positive class index of `edge_pred_from_emb`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "if clf_edge_pred_from_emb.classes_[0] == 1:\n",
-    "    positive_class_index = 0\n",
-    "else:\n",
-    "    positive_class_index = 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Evaluate the AUC score for the prediction with attri2vec embeddings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.798202033278801"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "roc_auc_score(out_of_sample_edges[:,2], edge_pred_from_emb[:,positive_class_index])"
+    "node_targets = [ node_data[\"subject\"][node_id] for node_id in node_ids ]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As the baseline, we also investigate the performance of node content features in predicting the edges of out-of-sample nodes. Firstly, we construct edge features from node content features with the same strategy."
+    "Transform the embeddings to 2d space for visualisation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "in_sample_edge_rep_from_feat = (node_data[feature_names].values[in_sample_edges[:,0]]-node_data[feature_names].values[in_sample_edges[:,1]])**2\n",
-    "out_of_sample_edge_rep_from_feat = (node_data[feature_names].values[out_of_sample_edges[:,0]]-node_data[feature_names].values[out_of_sample_edges[:,1]])**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we train the Logistic Regression classifier from in-sample edges with the edge features constructed from node content features."
+    "transform = TSNE # PCA\n",
+    "\n",
+    "trans = transform(n_components=2)\n",
+    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
-       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
+       "<Figure size 504x504 with 1 Axes>"
       ]
      },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clf_edge_pred_from_feat = LogisticRegression(verbose=0, solver='lbfgs', multi_class=\"auto\", max_iter=500)\n",
-    "clf_edge_pred_from_feat.fit(in_sample_edge_rep_from_feat, in_sample_edges[:,2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Predict the edge existence probability with the trained Logistic Regression classifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edge_pred_from_feat = clf_edge_pred_from_feat.predict_proba(out_of_sample_edge_rep_from_feat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get positive class index of `clf_edge_pred_from_feat`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if clf_edge_pred_from_feat.classes_[0] == 1:\n",
-    "    positive_class_index = 0\n",
-    "else:\n",
-    "    positive_class_index = 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Evaluate the AUC score for the prediction with node content features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6638897794650095"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "roc_auc_score(out_of_sample_edges[:,2], edge_pred_from_feat[:,positive_class_index])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "attri2vec performs much better than node content features in predicting the links of out-of-sample nodes."
+    "# draw the embedding points, coloring them by the target label (paper subject)\n",
+    "alpha = 0.7\n",
+    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
+    "node_colours = [ label_map[target] for target in node_targets ]\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plt.axes().set(aspect=\"equal\")\n",
+    "plt.scatter(node_embeddings_2d[:,0], \n",
+    "            node_embeddings_2d[:,1], \n",
+    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
+    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
+    "plt.show()"
    ]
   }
  ],

From 5e122413bb49dcedf588334b512db3cbf0d0c21a Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:00:04 +1000
Subject: [PATCH 27/82] Add attrivec citeseer embeddings demo

---
 .../stellargraph-attri2vec-citeseer.ipynb     | 684 ++++++++++++++++++
 1 file changed, 684 insertions(+)
 create mode 100644 demos/embeddings/stellargraph-attri2vec-citeseer.ipynb

diff --git a/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb b/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
new file mode 100644
index 000000000..41f6b60ad
--- /dev/null
+++ b/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
@@ -0,0 +1,684 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Node Representation Learning with attri2vec on Citeseer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
+    "\n",
+    "\n",
+    "## attri2vec\n",
+    "\n",
+    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
+    "\n",
+    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
+    "\n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "\n",
+    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import stellargraph as sg\n",
+    "from stellargraph.data import UnsupervisedSampler\n",
+    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
+    "from stellargraph.layer import Attri2Vec, link_classification\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "from pandas.core.indexes.base import Index\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset is the citation network Citeseer.\n",
+    "\n",
+    "It can be downloaded by clicking [here](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz)\n",
+    "\n",
+    "The following is the description of the dataset from the publisher,\n",
+    "> The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. The README file in the dataset provides more details.\n",
+    "\n",
+    "Download and unzip the citeseer.tgz file to a location on your computer. \n",
+    "\n",
+    "We assume that the dataset is stored in the directory\n",
+    "\n",
+    "`~/data/citeseer/`\n",
+    "\n",
+    "where the files `citeseer.cites` and `citeseer.content` can be located.\n",
+    "\n",
+    "We are going to load the data into a networkx object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"~/data/citeseer\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load edges in order 'cited-paper' <- 'citing-paper'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "citeseer_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.cites\"))\n",
+    "g_nx = nx.read_edgelist(path=citeseer_location, create_using=nx.DiGraph()).reverse()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert the graph to undirected graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g_nx = g_nx.to_undirected()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the node attribute data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  interactivity=interactivity, compiler=compiler, result=result)\n"
+     ]
+    }
+   ],
+   "source": [
+    "citeseer_data_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.content\"))\n",
+    "attr_names = [\"w_{}\".format(ii) for ii in range(3703)]\n",
+    "node_column_names =  attr_names + [\"subject\"]\n",
+    "node_attr = pd.read_csv(citeseer_data_location, sep='\\t', header=None, names=node_column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change the type of the indexes of node_attr to str."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_attr.index = Index(list(map(str, list(node_attr.index))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original graph contains some nodes with no attributes. We remove them here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g_nx = g_nx.subgraph(list(node_attr.index))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the largest connected component. For clarity we ignore isolated nodes and subgraphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Largest subgraph statistics: 2110 nodes, 3720 edges\n"
+     ]
+    }
+   ],
+   "source": [
+    "g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
+    "g_nx = max(g_nx_ccs, key=len)\n",
+    "print(\"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
+    "    g_nx.number_of_nodes(), g_nx.number_of_edges()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(g_nx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the ids of the nodes in the selected largest connected component. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_ids = sorted(list(g_nx.nodes))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_features = node_attr[attr_names].reindex(node_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = sg.StellarGraph(g_nx, node_features=node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 2110, Edges: 3720\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [2110]\n",
+      "    Edge types: paper-default->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-default->paper: [3720]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train attri2vec on Citeseer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = list(G.nodes())\n",
+    "number_of_walks = 4\n",
+    "length = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the batch size and the number of epochs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 50\n",
+    "epochs = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Attri2VecLinkGenerator with an estimated 1688 batches generated on the fly per epoch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
+    "\n",
+    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer_sizes = [128]\n",
+    "attri2vec = Attri2Vec(\n",
+    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0925 10:19:03.073729 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+      "W0925 10:19:03.096633 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
+    "x_inp, x_out = attri2vec.build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = link_classification(\n",
+    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
+    ")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
+    "    loss=keras.losses.binary_crossentropy,\n",
+    "    metrics=[keras.metrics.binary_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0925 10:19:03.339447 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1688/1688 [==============================] - 12s 7ms/step - loss: 0.6820 - binary_accuracy: 0.5580\n",
+      "Epoch 2/4\n",
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.5126 - binary_accuracy: 0.7584\n",
+      "Epoch 3/4\n",
+      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3356 - binary_accuracy: 0.8780\n",
+      "Epoch 4/4\n",
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2456 - binary_accuracy: 0.9161\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit_generator(\n",
+    "    train_gen,\n",
+    "    epochs=epochs,\n",
+    "    verbose=1,\n",
+    "    use_multiprocessing=False,\n",
+    "    workers=1,\n",
+    "    shuffle=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Visualise Node Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inp_src = x_inp[0]\n",
+    "x_out_src = x_out[0]\n",
+    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node embeddings by applying the learned mapping function to node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "43/43 [==============================] - 0s 2ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
+    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node subjects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_targets = [ node_attr[\"subject\"][node_id] for node_id in node_ids ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transform the embeddings to 2d space for visualisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform = TSNE # PCA\n",
+    "\n",
+    "trans = transform(n_components=2)\n",
+    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw the embedding points, coloring them by the target label (paper subject)\n",
+    "alpha = 0.7\n",
+    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
+    "node_colours = [ label_map[target] for target in node_targets ]\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plt.axes().set(aspect=\"equal\")\n",
+    "plt.scatter(node_embeddings_2d[:,0], \n",
+    "            node_embeddings_2d[:,1], \n",
+    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
+    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 67c3c452e89e1f7efbff4d254f12bdbd2c1da846 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:00:48 +1000
Subject: [PATCH 28/82] Add demos/node-classification/attri2vec/README.md file

---
 demos/node-classification/attri2vec/README.md | 63 +++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 demos/node-classification/attri2vec/README.md

diff --git a/demos/node-classification/attri2vec/README.md b/demos/node-classification/attri2vec/README.md
new file mode 100644
index 000000000..38bab7ac9
--- /dev/null
+++ b/demos/node-classification/attri2vec/README.md
@@ -0,0 +1,63 @@
+## Node classification using attri2vec [1]
+
+This folder contains two [Jupyter](http://jupyter.org/) python notebooks demonstrating the combined use of
+`stellargraph` (this library), `Scikit-learn` [3] libraries for node classification in two homogeneous graphs
+attached with node attributes.
+
+The first example demonstrates node representation learning and node classification using the citeseer
+paper citation network. This demo is included in the Jupyter notebook
+`attri2vec-citeseer-node-classification-example.ipynb`.
+
+The second example demonstrates node representation learning and node classification using the DBLP
+paper citation network. This demo is included in the Jupyter notebook
+`attri2vec-DBLP-node-classification-example.ipynb`.
+
+The two Jupyter notebooks include all the information for downloading the corresponding datasets, training the GraphSAGE
+models and using them to classify nodes with unknown (to the training algorithm) labels.
+
+To run the notebooks, install Jupyter to the same Python 3.6 environment as StellarGraph, following the instructions on
+the Jupyter project website: http://jupyter.org/install.html
+
+After starting the Jupyter server on your computer, load either of the two notebooks and follow the instructions inside.
+
+## Requirements
+
+All examples use Python 3.6 and the StellarGraph library. To install the StellarGraph library
+follow the instructions at: https://github.com/stellargraph/stellargraph
+
+Additional requirements are Pandas, Numpy and Scikit-Learn which are installed as dependencies
+of the StellarGraph library. In addition Juptyer is required to run the notebook version of
+the example.
+
+## Dataset
+
+The examples in this directory uses the citeseer and DBLP dataset.
+
+The citeseer dataset can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz).
+
+The following is the description of the dataset:
+> The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes.
+> The citation network consists of 4732 links. Each publication in the dataset is described by a
+> 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary.
+> The dictionary consists of 3703 unique words. The README file in the dataset provides more details.
+
+Download and unzip the [citeseer.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz) file to a location on your
+computer and pass this location as a command line argument to this script.
+
+The DBLP dataset can be downloaded from [here](https://www.kaggle.com/daozhang/dblp-subgraph).
+
+The following is the description of the dataset:
+> The DBLP citation network is a subgraph extracted from DBLP-Citation-network V3 (https://aminer.org/citation).
+> To form this subgraph, papers from four subjects are extracted according to their venue information:
+> Database, Data Mining, Artificial Intelligence and Computer Vision, and papers with no citations are removed.
+> The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct
+> 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word.
+> By ignoring the citation direction, we take the DBLP subgraph as an undirected network.
+
+Download and unzip the [DBLP.zip](https://www.kaggle.com/daozhang/dblp-subgraph) file to a location on your computer
+and pass this location as a command line argument to this script.
+
+## References
+
+**1.** Attributed Network Embedding via Subspace Discovery. D. Zhang, J, Yin, X. Zhu and C. Zhang, arXiv:1901.04095,
+[cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))

From 8fb54c1141dfa4d3cbe9182f39471ceb05d33db4 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:01:25 +1000
Subject: [PATCH 29/82] add attri2vec description to
 demos/node-classification/README.md file

---
 demos/node-classification/README.md | 33 +++++++++++++++++------------
 1 file changed, 20 insertions(+), 13 deletions(-)

diff --git a/demos/node-classification/README.md b/demos/node-classification/README.md
index 9dbfd31c6..f3d42bcc1 100644
--- a/demos/node-classification/README.md
+++ b/demos/node-classification/README.md
@@ -6,17 +6,22 @@ examples demonstrate using the `StellarGraph` library to build machine learning
 workflows on both homogeneous and heterogeneous networks.
 
 Each folder contains one or more examples of using the StellarGraph implementations of the
-state-of-the-art algorithms, GraphSAGE [3], HinSAGE, GCN [5], GAT [6], SGC [8], Node2Vec [1], and Metapath2Vec [2]. 
-GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [5]. Node2Vec and
+state-of-the-art algorithms, attri2vec[4], GraphSAGE [3], HinSAGE, GCN [6], GAT [7], SGC [9], Node2Vec [1], and Metapath2Vec [2].
+GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [6]. Node2Vec and
 Metapath2Vec are methods based on graph random walks and representation learning using the
-Word2Vec [4] algorithm.
+Word2Vec [5] algorithm. attri2vec[4] is also based on graph random walks and learns node
+representations by performing a mapping on node attributes.
 
 The examples folder structure is shown below.
 
+* [`/attri2vec`] (https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/graphsage)
+
+    Examples of supervised node classification for two homogeneous networks with attributes, using the attri2vec algorithm [4].
+
 * [`/graphsage`](https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/graphsage)
 
     Example of supervised node classification for a homogeneous network with attributed nodes, using the GraphSAGE algorithm [3].
-    
+
 * [`/gcn`](https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/gcn)
 
     Example of semi-supervised node classification for a homogeneous network, using the GCN algorithm [5].
@@ -42,28 +47,30 @@ The examples folder structure is shown below.
 
 ## References
 
-1. Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on 
+1. Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on
 Knowledge Discovery and Data Mining (KDD), 2016. ([link](https://snap.stanford.edu/node2vec/))
 
-2. Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and 
+2. Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and
 Ananthram Swami. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 135–144, 2017
 ([link](https://ericdongyx.github.io/metapath2vec/m2v.html))
 
-3. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 
+3. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216
 [cs.SI], 2017. ([link](http://snap.stanford.edu/graphsage/))
 
-4. Distributed representations of words and phrases and their compositionality. T. Mikolov, 
+4. Attributed Network Embedding via Subspace Discovery. D. Zhang, J, Yin, X. Zhu and C. Zhang, arXiv:1901.04095,
+[cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))
+
+5. Distributed representations of words and phrases and their compositionality. T. Mikolov,
 I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. In Advances in Neural Information Processing
  Systems (NIPS), pp. 3111-3119, 2013. ([link](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf))
 
-5. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. 
+6. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling.
 ICLR 2017. arXiv:1609.02907 ([link](https://arxiv.org/abs/1609.02907))
 
-6. Graph Attention Networks. P. Velickovic et al. ICLR 2018 ([link](https://arxiv.org/abs/1710.10903))
+7. Graph Attention Networks. P. Velickovic et al. ICLR 2018 ([link](https://arxiv.org/abs/1710.10903))
 
-7. On Calibration of Modern Neural Networks. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. 
+8. On Calibration of Modern Neural Networks. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger.
 ICML 2017. ([link](https://geoffpleiss.com/nn_calibration))
 
-8. Simplifying Graph Convolutional Networks. F. Wu, T. Zhang, A. H. de Souza, C. Fifty, T. Yu, and K. Q. Weinberger. 
+9. Simplifying Graph Convolutional Networks. F. Wu, T. Zhang, A. H. de Souza, C. Fifty, T. Yu, and K. Q. Weinberger.
 arXiv:1902.07153. ([link](https://arxiv.org/abs/1902.07153))
-

From 20046ec788389744c96e1be49fde47df7dfbd944 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:01:54 +1000
Subject: [PATCH 30/82] add demos/link-prediction/attri2vec/README.md file

---
 demos/link-prediction/attri2vec/README.md | 51 +++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 demos/link-prediction/attri2vec/README.md

diff --git a/demos/link-prediction/attri2vec/README.md b/demos/link-prediction/attri2vec/README.md
new file mode 100644
index 000000000..1fd1b57ba
--- /dev/null
+++ b/demos/link-prediction/attri2vec/README.md
@@ -0,0 +1,51 @@
+# attri2vec Link Prediction for Out-of-sample Nodes
+
+This is an example of using the attri2vec [1] model, with a link classifier on top,
+to predict links for out-of-sample nodes in a homogeneous citation network.
+
+In this demo, we first train the attri2vec model on the in-sample subgraph and infer
+representations for out-of-sample nodes with the trained attri2vec model. The we use the
+obtained node representations to perform link prediction for out-of-sample nodes.
+
+The link prediction problem is treated as a supervised binary classification problem for
+`(src, dst)` node pairs that make up links in the graph, with positive examples
+representing links that do exist in the graph, and negative examples representing
+links that don't.
+
+In this example, we learn to predict citation links between papers in a DBLP dataset (see below).
+
+## Requirements
+This example assumes the `stellargraph` library and its requirements have been
+installed by following the installation instructions in the README
+of the library's [root directory](https://github.com/stellargraph/stellargraph).
+
+## DBLP dataset
+
+This example is tested on the DBLP dataset. The attri2vec model assumes that node
+features are available.
+
+The following is the description of the dataset:
+> The DBLP citation network is a subgraph extracted from DBLP-Citation-network V3 (https://aminer.org/citation).
+> To form this subgraph, papers from four subjects are extracted according to their venue information:
+> Database, Data Mining, Artificial Intelligence and Computer Vision, and papers with no citations are removed.
+> The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct
+> 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word.
+> By ignoring the citation direction, we take the DBLP subgraph as an undirected network.
+
+Download and unzip the [DBLP.zip](https://www.kaggle.com/daozhang/dblp-subgraph) file to a location on your computer
+and pass this location as a command line argument to this script.
+
+## Running the notebook
+The narrated version of this example is available in the `stellargraph-attri2vec-DBLP.ipynb` notebook.
+To run the notebook:
+ - Activate the python 3.6 environment in which the
+`stellargraph` library is installed
+ - Start `jupyter-notebook`
+   - note: you may need to first install `jupyter` by running `pip install jupyter` in your python environment
+ - Navigate to the notebook (`/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb`), and click on
+ it to launch the notebook.
+
+## References
+
+[1] Attributed Network Embedding via Subspace Discovery. D. Zhang, J, Yin, X. Zhu and C. Zhang, arXiv:1901.04095,
+[cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))

From 4121e333c56e7c960fc6d319f67bf30d3965e626 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:02:23 +1000
Subject: [PATCH 31/82] add attri2vec description to
 demos/link-prediction/README.md file

---
 demos/link-prediction/README.md | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/demos/link-prediction/README.md b/demos/link-prediction/README.md
index b639e6515..77a98fb58 100644
--- a/demos/link-prediction/README.md
+++ b/demos/link-prediction/README.md
@@ -6,10 +6,11 @@ examples demonstrate using the `StellarGraph` library to build machine learning
 workflows on both homogeneous and heterogeneous networks.
 
 Each folder contains one or more examples of using the StellarGraph implementations of the
-state-of-the-art algorithms, GraphSAGE [3], HinSAGE, GCN [5], GAT [6], Node2Vec [1], and Metapath2Vec [2]. 
-GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [5]. Node2Vec and
+state-of-the-art algorithms, attri2vec[4], GraphSAGE [3], HinSAGE, GCN [6], GAT [7], Node2Vec [1], and Metapath2Vec [2].
+GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [6]. Node2Vec and
 Metapath2Vec are methods based on graph random walks and representation learning using the
-Word2Vec [4] algorithm.
+Word2Vec [5] algorithm. attri2vec[4] is also based on graph random walks and learns node
+representations by performing a mapping on node attributes.
 
 The examples folder structure is shown below.
 
@@ -28,27 +29,34 @@ The examples folder structure is shown below.
     Example of supervised link attribute prediction for a heterogeneous network with attributed nodes of different types,
     using the HinSAGE algorithm.
 
+* [`/attri2vec`] (https://github.com/stellargraph/stellargraph/tree/master/demos/link-prediction/attri2vec)
+
+    Example of link prediction for out-of-sample nodes for a homogeneous network with attributed nodes,
+    using the attri2vec algorithm.
 
 ## References
 
-1. Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on 
+1. Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on
 Knowledge Discovery and Data Mining (KDD), 2016. ([link](https://snap.stanford.edu/node2vec/))
 
-2. Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and 
+2. Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and
 Ananthram Swami. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 135–144, 2017
 ([link](https://ericdongyx.github.io/metapath2vec/m2v.html))
 
-3. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 
+3. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216
 [cs.SI], 2017. ([link](http://snap.stanford.edu/graphsage/))
 
-4. Distributed representations of words and phrases and their compositionality. T. Mikolov, 
+4. Attributed Network Embedding via Subspace Discovery. D. Zhang, J, Yin, X. Zhu and C. Zhang, arXiv:1901.04095,
+[cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))
+
+5. Distributed representations of words and phrases and their compositionality. T. Mikolov,
 I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. In Advances in Neural Information Processing
  Systems (NIPS), pp. 3111-3119, 2013. ([link](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf))
 
-5. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. 
+6. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling.
 ICLR 2017. arXiv:1609.02907 ([link](https://arxiv.org/abs/1609.02907))
 
-6. Graph Attention Networks. P. Velickovic et al. ICLR 2018 ([link](https://arxiv.org/abs/1710.10903))
+7. Graph Attention Networks. P. Velickovic et al. ICLR 2018 ([link](https://arxiv.org/abs/1710.10903))
 
-7. On Calibration of Modern Neural Networks. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. 
+8. On Calibration of Modern Neural Networks. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger.
 ICML 2017. ([link](https://geoffpleiss.com/nn_calibration))

From 7b65cab5026f850f9c6f90f07792d856e329e48b Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:02:53 +1000
Subject: [PATCH 32/82] add attri2vec description to demos/embeddings/README.md
 file

---
 demos/embeddings/README.md | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/demos/embeddings/README.md b/demos/embeddings/README.md
index 18f4aa190..1a7ce176d 100644
--- a/demos/embeddings/README.md
+++ b/demos/embeddings/README.md
@@ -1,8 +1,8 @@
 ## Representation Learning Examples
 
-This folder contains three [Jupyter](http://jupyter.org/) python notebooks demonstrating the use of unsupervised graph representation learning methods implemented in the `stellargraph` library for homogeneous and hetrogenous graphs with or without node features. The original works are referenced below. 
+This folder contains three [Jupyter](http://jupyter.org/) python notebooks demonstrating the use of unsupervised graph representation learning methods implemented in the `stellargraph` library for homogeneous and hetrogenous graphs with or without node features. The original works are referenced below.
 
-**Node2Vec** and **Metapath2Vec** notebooks demonstrate the combined use of `stellargraph` and `Gensim` [4] libraries for representation learning on homogeneous and heterogeneous graphs. 
+**Node2Vec** and **Metapath2Vec** notebooks demonstrate the combined use of `stellargraph` and `Gensim` [4] libraries for representation learning on homogeneous and heterogeneous graphs.
 **Unsupervised GraphSAGE** notebook demonstrates the use of `Stellargraph` library's GraphSAGE implementation for unsupervised learning of node embeddings for homogeneous graphs with node features.
 **attri2vec** notebook demonstrates the implementation of attri2vec with the `Stellargraph` library for unsupervised inductive learning of node embeddings for homogeneous graphs with node features, and the evaluation for its ability to infer the representations of out-of-sample nodes with the out-of-sample node link prediction task.   
 
@@ -10,24 +10,25 @@ The notebooks demonstrate the following algorithms.
 - `stellargraph-node2vec.ipynb` The **Node2Vec** algorithm [1] for representation learning on homogeneous graphs
 - `stellargraph-metapath2vec.ipynb` The **Metapath2Vec** algorithm [2] for representation learning on heterogeneous graphs.
 - `embeddings-unsupervised-graphsage-cora.ipynb` The **Unsupervised GraphSAGE** algorithm [5] for representation learning on homogeneous graphs with node features.
-- `stellargraph-attri2vec-DBLP.ipynb` The **attri2vec** algorithm [6] for representation learning on homogeneous graphs with node features.
+- `stellargraph-attri2vec-citeseer.ipynb` The **attri2vec** algorithm [6] for representation learning on the homogeneous graph citeseer with node features.
+- `stellargraph-attri2vec-DBLP.ipynb` The **attri2vec** algorithm [6] for representation learning on the homogeneous graph DBLP with node features.
 
-All examples demonstrate how to calculate embedding vectors for a graph's nodes in just a few lines of Python code. 
+All examples demonstrate how to calculate embedding vectors for a graph's nodes in just a few lines of Python code.
 The learned node representations can be used in numerous downstream tasks such as node attribute inference, link
 prediction, and community detection.
 
 
 ## References
 
-**1.** Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference 
+**1.** Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference
 on Knowledge Discovery and Data Mining (KDD), 2016. ([link](https://snap.stanford.edu/node2vec/))
 
-**2.**  Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and 
-Ananthram Swami. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 135–144, 2017. 
+**2.**  Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and
+Ananthram Swami. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 135–144, 2017.
 ([link](https://ericdongyx.github.io/papers/KDD17-dong-chawla-swami-metapath2vec.pdf))
 
-**3.** Distributed representations of words and phrases and their compositionality. T. Mikolov, I. Sutskever, K. Chen, 
-G. S. Corrado, and J. Dean.  In Advances in Neural Information Processing Systems (NIPS), pp. 3111-3119, 2013. 
+**3.** Distributed representations of words and phrases and their compositionality. T. Mikolov, I. Sutskever, K. Chen,
+G. S. Corrado, and J. Dean.  In Advances in Neural Information Processing Systems (NIPS), pp. 3111-3119, 2013.
 ([link](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf))
 
 **4.** Gensim: Topic modelling for humans. ([link](https://radimrehurek.com/gensim/))
@@ -35,4 +36,5 @@ G. S. Corrado, and J. Dean.  In Advances in Neural Information Processing System
 **5.** Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216
 [cs.SI], 2017. ([link](http://snap.stanford.edu/graphsage/))
 
-**6.** Attributed Network Embedding via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))
+**6.** Attributed Network Embedding via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095,
+[cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))

From c68a62d4cd044066494d012247e23e8a534eb4e9 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:04:06 +1000
Subject: [PATCH 33/82] add attri2vec description to demos/README.md file

---
 demos/README.md | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/demos/README.md b/demos/README.md
index 970549801..12c117cd5 100644
--- a/demos/README.md
+++ b/demos/README.md
@@ -7,10 +7,10 @@ examples demonstrate using the `StellarGraph` library to build machine learning
 workflows on both homogeneous and heterogeneous networks.
 
 Each folder contains one or more examples of using the StellarGraph implementations of the
-state-of-the-art algorithms, GraphSAGE [3], HinSAGE, GCN [5], GAT [6], Node2Vec [1], and Metapath2Vec [2]. 
-GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [5]. Node2Vec and
+state-of-the-art algorithms, attri2vec [4], GraphSAGE [3], HinSAGE, GCN [6], GAT [7], Node2Vec [1], and Metapath2Vec [2].
+GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [6]. Node2Vec and
 Metapath2Vec are methods based on graph random walks and representation learning using the
-Word2Vec [4] algorithm.
+Word2Vec [5] algorithm.
 
 The examples folder structure is shown below.
 
@@ -30,8 +30,8 @@ The examples folder structure is shown below.
 * [`/ensembles`](https://github.com/stellargraph/stellargraph/tree/master/demos/ensembles)
 
     Examples of using ensembles of graph convolutional neural networks, e.g., GraphSAGE, GCN, HinSAGE, etc., for
-    node classification and link prediction. Model ensembles usually yield better predictions than single models, 
-    while also providing estimates of prediction uncertainty as a bonus. 
+    node classification and link prediction. Model ensembles usually yield better predictions than single models,
+    while also providing estimates of prediction uncertainty as a bonus.
 
 * [`/calibration`](https://github.com/stellargraph/stellargraph/tree/master/demos/calibration)
 
@@ -49,24 +49,27 @@ The examples folder structure is shown below.
 
 ## References
 
-1. Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on 
+1. Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on
 Knowledge Discovery and Data Mining (KDD), 2016. ([link](https://snap.stanford.edu/node2vec/))
 
-2. Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and 
+2. Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and
 Ananthram Swami. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 135–144, 2017
 ([link](https://ericdongyx.github.io/metapath2vec/m2v.html))
 
-3. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 
+3. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216
 [cs.SI], 2017. ([link](http://snap.stanford.edu/graphsage/))
 
-4. Distributed representations of words and phrases and their compositionality. T. Mikolov, 
+4. Attributed Network Embedding via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095,
+[cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))
+
+5. Distributed representations of words and phrases and their compositionality. T. Mikolov,
 I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. In Advances in Neural Information Processing
  Systems (NIPS), pp. 3111-3119, 2013. ([link](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf))
 
-5. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. 
+6. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling.
 ICLR 2017. arXiv:1609.02907 ([link](https://arxiv.org/abs/1609.02907))
 
-6. Graph Attention Networks. P. Velickovic et al. ICLR 2018 ([link](https://arxiv.org/abs/1710.10903))
+7. Graph Attention Networks. P. Velickovic et al. ICLR 2018 ([link](https://arxiv.org/abs/1710.10903))
 
-7. On Calibration of Modern Neural Networks. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. 
+8. On Calibration of Modern Neural Networks. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger.
 ICML 2017. ([link](https://geoffpleiss.com/nn_calibration))

From a00b4cd3c42fe9753879bdf47526c2ac1ff22419 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:04:27 +1000
Subject: [PATCH 34/82] add attri2vec description to README.md file

---
 README.md | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 63c55800c..18bf87413 100644
--- a/README.md
+++ b/README.md
@@ -73,7 +73,7 @@ The StellarGraph library can be used to solve tasks using graph-structured data,
 - Representation learning for nodes and edges, to be used for visualisation and various downstream machine learning tasks;
 - Classification and attribute inference of nodes or edges;
 - Link prediction;
-- Interpretation of node classification through calculated importances of edges and neighbours for selected nodes [7].
+- Interpretation of node classification through calculated importances of edges and neighbours for selected nodes [8].
 
 We provide [examples](https://github.com/stellargraph/stellargraph/tree/master/demos/) of using `StellarGraph` to solve such tasks using several real-world datasets.
 
@@ -130,7 +130,7 @@ can be downloaded and installed from [python.org](https://python.org/). Alternat
 environment, available from [anaconda.com](https://www.anaconda.com/download/).
 
 *Note*: while the library works on Python 3.7 it is based on Keras which does not officially support Python 3.7.
-Therefore, there may be unforseen bugs and you there are many warnings from the Python libraries that 
+Therefore, there may be unforseen bugs and you there are many warnings from the Python libraries that
 StellarGraph depends upon.
 
 <!--
@@ -172,7 +172,7 @@ pip install .[demos]
 
 * [stellargraph/stellargraph](https://hub.docker.com/r/stellargraph/stellargraph): Docker image with `stellargraph` installed.
 
-Images can be pulled via `docker pull stellargraph/stellargraph` 
+Images can be pulled via `docker pull stellargraph/stellargraph`
 
 
 ## Running the examples
@@ -193,13 +193,16 @@ The StellarGraph library currently includes the following algorithms for graph m
   The current implementation supports mean aggregation of neighbour nodes,
   taking into account their types and the types of links between them.
 
-* The Graph ATtention Network (GAT) [4]
+* Attri2vec [4]
+  - Supports node representation learning, node classification, and out-of-sample node link prediction for homogeneous graphs with node attributes.
+
+* The Graph ATtention Network (GAT) [5]
   - The GAT algorithm supports representation learning and node classification for homogeneous graphs. There are versions of the graph attention layer that support both sparse and dense adjacency matrices.
 
-* Graph Convolutional Network (GCN) [5]
+* Graph Convolutional Network (GCN) [6]
   - The GCN algorithm supports representation learning and node classification for homogeneous graphs. There are versions of the graph convolutional layer that support both sparse and dense adjacency matrices.
 
-* Simplified Graph Convolutional network (SGC) [6]
+* Simplified Graph Convolutional network (SGC) [7]
   - The SGC network algorithm supports representation learning and node classification for homogeneous graphs. It is an extension of the GCN algorithm that smooths the graph to bring in more distant neighbours of nodes without using multiple layers.
 
 * Node2Vec [2]
@@ -262,14 +265,17 @@ Neural Information Processing Systems (NIPS), 2017. ([link](https://arxiv.org/ab
 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 135–144, 2017
 ([link](https://ericdongyx.github.io/metapath2vec/m2v.html))
 
-4. Graph Attention Networks. P. Velickovic et al.
+4. Attributed Network Embedding via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095,
+[cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))
+
+5. Graph Attention Networks. P. Velickovic et al.
 International Conference on Learning Representations (ICLR) 2018 ([link](https://arxiv.org/abs/1710.10903))
 
-5. Graph Convolutional Networks (GCN): Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling.
+6. Graph Convolutional Networks (GCN): Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling.
 International Conference on Learning Representations (ICLR), 2017
 ([link](https://github.com/tkipf/gcn))
 
-6. Simplifying Graph Convolutional Networks. F. Wu, T. Zhang, A. H. de Souza, C. Fifty, T. Yu, and K. Q. Weinberger. 
+7. Simplifying Graph Convolutional Networks. F. Wu, T. Zhang, A. H. de Souza, C. Fifty, T. Yu, and K. Q. Weinberger.
 International Conference on Machine Learning (ICML), 2019. ([link](https://arxiv.org/abs/1902.07153))
 
-7. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. IJCAI 2019. ([link](https://arxiv.org/abs/1903.01610))
+8. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. IJCAI 2019. ([link](https://arxiv.org/abs/1903.01610))

From c0922ea5cf500f117f605982bb928207ba06355b Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:06:01 +1000
Subject: [PATCH 35/82] code update for node_mappers.py

---
 stellargraph/mapper/node_mappers.py | 73 +++++++++++++++++------------
 1 file changed, 43 insertions(+), 30 deletions(-)

diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py
index b94fc0db4..328f7595b 100755
--- a/stellargraph/mapper/node_mappers.py
+++ b/stellargraph/mapper/node_mappers.py
@@ -35,9 +35,9 @@
 import itertools as it
 import networkx as nx
 import scipy.sparse as sps
-import keras.backend as K
+import tensorflow.keras.backend as K
 from functools import reduce
-from keras.utils import Sequence
+from tensorflow.keras.utils import Sequence
 
 from ..data.explorer import (
     SampledBreadthFirstWalk,
@@ -105,22 +105,20 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
                 "Head nodes supplied to generator contain IDs not found in graph"
             )
 
-        # Infer head_node_type
         if (
-            generator.schema.node_type_map is None
-            or generator.schema.edge_type_map is None
+            isinstance(generator, GraphSAGENodeGenerator)
+            or isinstance(generator, HinSAGENodeGenerator)
+            or isinstance(generator, Attri2VecNodeGenerator)
         ):
-            raise RuntimeError("Schema must have node and edge type maps.")
+            self.generator = generator
         else:
-            head_node_types = {generator.schema.get_node_type(n) for n in ids}
-        if len(head_node_types) > 1:
-            raise ValueError(
-                "Only a single head node type is currently supported for HinSAGE models"
+            raise TypeError(
+                "({}) GraphSAGENodeGenerator, HinSAGENodeGenerator or Attri2VecNodeGenerator is required.".format(
+                    type(self).__name__
+                )
             )
-        head_node_type = head_node_types.pop()
 
         # Store the generator to draw samples from graph
-        self.generator = generator
         self.ids = list(ids)
         self.data_size = len(self.ids)
         self.shuffle = shuffle
@@ -128,11 +126,28 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
         # Shuffle IDs to start
         self.on_epoch_end()
 
-        # Save head node type and generate sampling schema
-        self.head_node_types = [head_node_type]
-        self._sampling_schema = generator.schema.sampling_layout(
-            self.head_node_types, generator.num_samples
-        )
+        if isinstance(self.generator, GraphSAGENodeGenerator) or isinstance(
+            self.generator, HinSAGENodeGenerator
+        ):
+            # Infer head_node_type
+            if (
+                generator.schema.node_type_map is None
+                or generator.schema.edge_type_map is None
+            ):
+                raise RuntimeError("Schema must have node and edge type maps.")
+            else:
+                head_node_types = {generator.schema.get_node_type(n) for n in ids}
+            if len(head_node_types) > 1:
+                raise ValueError(
+                    "Only a single head node type is currently supported for HinSAGE models"
+                )
+            head_node_type = head_node_types.pop()
+
+            # Save head node type and generate sampling schema
+            self.head_node_types = [head_node_type]
+            self._sampling_schema = generator.schema.sampling_layout(
+                self.head_node_types, generator.num_samples
+            )
 
     def __len__(self):
         """Denotes the number of batches per epoch"""
@@ -166,8 +181,16 @@ def __getitem__(self, batch_num):
         # Get corresponding targets
         batch_targets = None if self.targets is None else self.targets[batch_indices]
 
-        # Get sampled nodes
-        batch_feats = self.generator.sample_features(head_ids, self._sampling_schema)
+        if isinstance(self.generator, GraphSAGENodeGenerator) or isinstance(
+            self.generator, HinSAGENodeGenerator
+        ):
+            # Get sampled nodes for GraphSAGENodeGenerator and HinSAGENodeGenerator
+            batch_feats = self.generator.sample_features(
+                head_ids, self._sampling_schema
+            )
+        else:
+            # Get sampled nodes for Attri2VecNodeGenerator
+            batch_feats = self.generator.sample_features(head_ids)
 
         return batch_feats, batch_targets
 
@@ -544,23 +567,13 @@ def __init__(self, G, batch_size, name=None):
             raise TypeError("Graph must be a StellarGraph object.")
 
         self.graph = G
-        self.num_samples = [0]  # for compatibility with GraphSAGE
         self.batch_size = batch_size
         self.name = name
 
         # Check if the graph has features
         G.check_graph_for_ml()
 
-        # We need a schema for compatibility with HinSAGE
-        self.schema = G.create_graph_schema(create_type_maps=True)
-
-        # Check that there is only a single node type for Attri2Vec
-        if len(self.schema.node_types) > 1:
-            print(
-                "Warning: running homogeneous Attri2Vec on a graph with multiple node types"
-            )
-
-    def sample_features(self, head_nodes, sampling_schema):
+    def sample_features(self, head_nodes):
         """
         Sample content features of the head nodes, and return these as a list of feature 
         arrays for the attri2vec algorithm.

From 645ca32f2f8c8133263584045dd02507e8f60dbd Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:06:16 +1000
Subject: [PATCH 36/82] code update for link_mappers.py

---
 stellargraph/mapper/link_mappers.py | 126 ++++++++++++++++++----------
 1 file changed, 80 insertions(+), 46 deletions(-)

diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
index 1f3e5b750..a69f14756 100755
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -35,8 +35,7 @@
 import collections
 from functools import reduce
 
-import keras
-from keras.utils import Sequence
+from tensorflow.keras.utils import Sequence
 from stellargraph.core.graph import StellarGraphBase
 from stellargraph.data.explorer import (
     SampledBreadthFirstWalk,
@@ -92,7 +91,19 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
         if targets is not None and len(ids) != len(targets):
             raise ValueError("Length of link ids must match length of link targets")
 
-        self.generator = generator
+        if (
+            isinstance(generator, GraphSAGELinkGenerator)
+            or isinstance(generator, HinSAGELinkGenerator)
+            or isinstance(generator, Attri2VecLinkGenerator)
+        ):
+            self.generator = generator
+        else:
+            raise TypeError(
+                "({}) GraphSAGELinkGenerator, HinSAGELinkGenerator or Attri2VecLinkGenerator is required.".format(
+                    type(self).__name__
+                )
+            )
+
         self.ids = list(ids)
         self.data_size = len(self.ids)
         self.shuffle = shuffle
@@ -100,17 +111,20 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
         # Shuffle the IDs to begin
         self.on_epoch_end()
 
-        # Get head node types from all src, dst nodes extracted from all links,
-        # and make sure there's only one pair of node types:
-        self.head_node_types = self._infer_head_node_types(generator.schema)
+        if isinstance(self.generator, GraphSAGELinkGenerator) or isinstance(
+            self.generator, HinSAGELinkGenerator
+        ):
+            # Get head node types from all src, dst nodes extracted from all links,
+            # and make sure there's only one pair of node types:
+            self.head_node_types = self._infer_head_node_types(generator.schema)
 
-        self._sampling_schema = generator.schema.sampling_layout(
-            self.head_node_types, generator.num_samples
-        )
+            self._sampling_schema = generator.schema.sampling_layout(
+                self.head_node_types, generator.num_samples
+            )
 
-        self.type_adjacency_list = generator.schema.type_adjacency_list(
-            self.head_node_types, len(generator.num_samples)
-        )
+            self.type_adjacency_list = generator.schema.type_adjacency_list(
+                self.head_node_types, len(generator.num_samples)
+            )
 
     def _infer_head_node_types(self, schema):
         """Get head node types from all src, dst nodes extracted from all links in self.ids"""
@@ -156,8 +170,16 @@ def __getitem__(self, batch_num):
         # Get targets for nodes
         batch_targets = None if self.targets is None else self.targets[batch_indices]
 
-        # Get sampled nodes
-        batch_feats = self.generator.sample_features(head_ids, self._sampling_schema)
+        if isinstance(self.generator, GraphSAGELinkGenerator) or isinstance(
+            self.generator, HinSAGELinkGenerator
+        ):
+            # Get sampled nodes for GraphSAGELinkGenerator and HinSAGELinkGenerator
+            batch_feats = self.generator.sample_features(
+                head_ids, self._sampling_schema
+            )
+        else:
+            # Get sampled nodes for Attri2VecLinkGenerator
+            batch_feats = self.generator.sample_features(head_ids)
 
         return batch_feats, batch_targets
 
@@ -187,14 +209,23 @@ class OnDemandLinkSequence(Sequence):
 
     def __init__(self, generator, walker):
 
-        self.generator = generator  # graphlinkgenerator instance
-
-        self.head_node_types = self.generator.schema.node_types * 2
+        if isinstance(generator, GraphSAGELinkGenerator) or isinstance(
+            generator, Attri2VecLinkGenerator
+        ):
+            self.generator = generator
+        else:
+            raise TypeError(
+                "({}) GraphSAGELinkGenerator or Attri2VecLinkGenerator is required.".format(
+                    type(self).__name__
+                )
+            )
 
-        # YT: we need to have self._sampling_schema for GraphSAGE.build() method to work
-        self._sampling_schema = generator.schema.sampling_layout(
-            self.head_node_types, generator.num_samples
-        )
+        if isinstance(self.generator, GraphSAGELinkGenerator):
+            self.head_node_types = self.generator.schema.node_types * 2
+            # YT: we need to have self._sampling_schema for GraphSAGE.build() method to work
+            self._sampling_schema = generator.schema.sampling_layout(
+                self.head_node_types, generator.num_samples
+            )
 
         if isinstance(walker, UnsupervisedSampler):
 
@@ -251,24 +282,30 @@ def __getitem__(self, batch_num):
         # Get head nodes and labels
         head_ids, batch_targets = next(self._gen)
 
-        if self.head_node_types is None:
+        if isinstance(self.generator, GraphSAGELinkGenerator):
+            if self.head_node_types is None:
 
-            # Get head node types from all src, dst nodes extracted from all links,
-            # and make sure there's only one pair of node types:
-            self.head_node_types = self._infer_head_node_types(
-                self.generator.schema, head_ids
-            )
+                # Get head node types from all src, dst nodes extracted from all links,
+                # and make sure there's only one pair of node types:
+                self.head_node_types = self._infer_head_node_types(
+                    self.generator.schema, head_ids
+                )
 
-            self._sampling_schema = self.generator.schema.sampling_layout(
-                self.head_node_types, self.generator.num_samples
-            )
+                self._sampling_schema = self.generator.schema.sampling_layout(
+                    self.head_node_types, self.generator.num_samples
+                )
 
-            self.type_adjacency_list = self.generator.schema.type_adjacency_list(
-                self.head_node_types, len(self.generator.num_samples)
-            )
+                self.type_adjacency_list = self.generator.schema.type_adjacency_list(
+                    self.head_node_types, len(self.generator.num_samples)
+                )
 
-        # Get sampled nodes
-        batch_feats = self.generator.sample_features(head_ids, self._sampling_schema)
+            # Get sampled nodes for GraphSAGELinkGenerator
+            batch_feats = self.generator.sample_features(
+                head_ids, self._sampling_schema
+            )
+        else:
+            # Get sampled nodes for Attri2VecLinkGenerator
+            batch_feats = self.generator.sample_features(head_ids)
 
         return batch_feats, batch_targets
 
@@ -650,32 +687,27 @@ def __init__(self, G, batch_size, name=None):
         G.check_graph_for_ml(features=True)
 
         self.graph = G
-        self.num_samples = [0]  # for compatibility with GraphSAGE
         self.batch_size = batch_size
         self.name = name
 
-        # We need a schema for compatibility with HinSAGE
-        self.schema = G.create_graph_schema(create_type_maps=True)
-
-    def sample_features(self, head_links, sampling_schema):
+    def sample_features(self, head_links):
         """
         Sample content features of the target nodes and the ids of the context nodes
         and return these as a list of feature arrays for the attri2vec algorithm.
 
         Args:
             head_links: An iterable of edges to perform sampling for.
-            sampling_schema: The sampling schema for the model, for compatibility with GraphSAGE and HinSAGE.
 
         Returns:
             A list of feaure arrys, with each element being the feature of a
             target node and the id of the corresponding context node.
         """
 
-        all_nodes = list(self.graph.nodes())
         target_ids = [head_link[0] for head_link in head_links]
-        context_ids = [all_nodes.index(head_link[1]) for head_link in head_links]
+        context_ids = [head_link[1] for head_link in head_links]
         target_feats = self.graph.get_feature_for_nodes(target_ids)
-        batch_feats = [target_feats, np.array(context_ids)]
+        context_feats = self.graph.get_index_for_nodes(context_ids)
+        batch_feats = [target_feats, np.array(context_feats)]
 
         return batch_feats
 
@@ -702,8 +734,10 @@ def flow(self, link_ids, targets=None, shuffle=False):
                 method to generate samples on the fly.
             targets (optional, array): a 2D array of numeric targets with shape
                 `(len(link_ids), target_size)`.
-            shuffle (optional, bool): If True the node_ids will be shuffled at each
-                epoch, if False the node_ids will be processed in order.
+            shuffle (optional, bool): The value of 'shuffle' is only relevant if 'link_ids' 
+                is an Iterable and it is ignored if the latter is an instance of UnsupervisedSampler. 
+                If True the node_ids will be shuffled at each epoch, if False the node_ids will be 
+                processed in order.
 
         Returns:
             An OnDemandLinkSequence object to use with the attri2vec model.

From c0dbb1f2738fc82bd09fc34546083907ba66eb29 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:06:40 +1000
Subject: [PATCH 37/82] code update for link_inference.py

---
 stellargraph/layer/link_inference.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/stellargraph/layer/link_inference.py b/stellargraph/layer/link_inference.py
index ab03ef308..44a6a042f 100644
--- a/stellargraph/layer/link_inference.py
+++ b/stellargraph/layer/link_inference.py
@@ -20,7 +20,7 @@
 """
 
 from typing import AnyStr, Optional, List, Tuple
-from keras.layers import (
+from tensorflow.keras.layers import (
     Layer,
     Concatenate,
     Dense,
@@ -30,7 +30,7 @@
     Reshape,
     Activation,
 )
-from keras import backend as K
+from tensorflow.keras import backend as K
 import warnings
 
 

From bbe57002d52a8f9d35f25bd891d33238b587c242 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:07:24 +1000
Subject: [PATCH 38/82] add a function of get_index_for_nodes to graph.py

---
 stellargraph/core/graph.py | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/stellargraph/core/graph.py b/stellargraph/core/graph.py
index 4c9038250..cd45e59f1 100644
--- a/stellargraph/core/graph.py
+++ b/stellargraph/core/graph.py
@@ -433,6 +433,44 @@ def check_graph_for_ml(self, features=True):
 
         # TODO: check the feature node_ids against the graph node ids?
 
+    def get_index_for_nodes(self, nodes, node_type=None):
+        """
+        Get the indices for the specified node or nodes.
+        If the node type is not specified the node types will be found
+        for all nodes. It is therefore important to supply the ``node_type``
+        for this method to be fast.
+
+        Args:
+            n: (list or hashable) Node ID or list of node IDs
+            node_type: (hashable) the type of the nodes.
+
+        Returns:
+            Numpy array containing the indices for the requested nodes.
+        """
+        if not is_real_iterable(nodes):
+            nodes = [nodes]
+
+        # Get the node type if not specified.
+        if node_type is None:
+            node_types = {
+                self._get_node_type(self.node[n]) for n in nodes if n is not None
+            }
+
+            if len(node_types) > 1:
+                raise ValueError("All nodes must be of the same type.")
+
+            if len(node_types) == 0:
+                raise ValueError(
+                    "At least one node must be given if node_type not specified"
+                )
+
+            node_type = node_types.pop()
+
+        # Get index for nodes of this type
+        nt_id_to_index = self._node_index_maps[node_type]
+        node_indices = [nt_id_to_index.get(n) for n in nodes]
+        return node_indices
+
     def get_feature_for_nodes(self, nodes, node_type=None):
         """
         Get the numeric feature vectors for the specified node or nodes.

From 9639b4d1c74e39cb58e2b46a34c63a78f23abdb6 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:07:45 +1000
Subject: [PATCH 39/82] code update for attri2vec.py

---
 stellargraph/layer/attri2vec.py | 57 ++++++++++++---------------------
 1 file changed, 20 insertions(+), 37 deletions(-)

diff --git a/stellargraph/layer/attri2vec.py b/stellargraph/layer/attri2vec.py
index ec4774357..3f30e90ae 100644
--- a/stellargraph/layer/attri2vec.py
+++ b/stellargraph/layer/attri2vec.py
@@ -21,10 +21,9 @@
 """
 __all__ = ["Attri2Vec"]
 
-from keras import Input
-from keras import backend as K
-from keras.layers import Input, Dense, Lambda, Dropout, Reshape
-from keras.layers.embeddings import Embedding
+from tensorflow.keras import Input
+from tensorflow.keras import backend as K
+from tensorflow.keras.layers import Dense, Lambda, Reshape, Embedding
 import warnings
 
 
@@ -42,8 +41,9 @@ class Attri2Vec:
         input_dim (int): The dimensions of the node features used as input to the model.
         node_num (int): The number of nodes in the given graph.
         bias (bool): If True a bias vector is learnt for each layer in the attri2vec model, default to False.
-        dropout (float): The dropout supplied to each layer in the attri2vec model, default to 0.0.
-        normalize (str or None): The normalization used after each layer, default to None.
+        activation (str): The activation function of each layer in the attri2vec model, which takes values from 
+        "linear", "relu" and "sigmoid"(default).
+        normalize ("l2" or None): The normalization used after each layer, default to None.
 
     """
 
@@ -54,25 +54,33 @@ def __init__(
         input_dim=None,
         node_num=None,
         bias=False,
-        dropout=0.0,
+        activation="sigmoid",
         normalize=None,
     ):
 
+        if activation == "linear" or activation == "relu" or activation == "sigmoid":
+            self.activation = activation
+        else:
+            raise ValueError(
+                "Activation should be either 'linear', 'relu' or 'sigmoid'; received '{}'".format(
+                    activation
+                )
+            )
+
         if normalize == "l2":
             self._normalization = Lambda(lambda x: K.l2_normalize(x, axis=-1))
 
-        elif normalize is None or normalize == "none" or normalize == "None":
+        elif normalize is None:
             self._normalization = Lambda(lambda x: x)
 
         else:
             raise ValueError(
-                "Normalization should be either 'l2' or 'none'; received '{}'".format(
+                "Normalization should be either 'l2' or None; received '{}'".format(
                     normalize
                 )
             )
 
         # Get the input_dim and node_num from the generator if it is given
-        # Use both the schema and head node type from the generator
         self.generator = generator
         if generator is not None:
             feature_sizes = generator.generator.graph.node_feature_sizes()
@@ -96,11 +104,6 @@ def __init__(
         # Model parameters
         self.n_layers = len(layer_sizes)
         self.bias = bias
-        self.dropout = dropout
-        self.activation = "sigmoid"  # the activation fuction used in hidden layers
-        self.initializer = (
-            "glorot_uniform"
-        )  # the initializer for the weights to construct hidden layers
 
         # Feature dimensions for each layer
         self.dims = [self.input_feature_size] + layer_sizes
@@ -118,12 +121,8 @@ def __call__(self, xin):
         # Form Attri2Vec layers iteratively
         h_layer = xin
         for layer in range(0, self.n_layers):
-            h_layer = Dropout(self.dropout)(h_layer)
             h_layer = Dense(
-                self.dims[layer + 1],
-                activation=self.activation,
-                kernel_initializer=self.initializer,
-                use_bias=self.bias,
+                self.dims[layer + 1], activation=self.activation, use_bias=self.bias
             )(h_layer)
             h_layer = self._normalization(h_layer)
 
@@ -185,23 +184,7 @@ def build(self):
             model output tensor(s) of shape (batch_size, layer_sizes[-1])
 
         """
-        if self.generator is not None and hasattr(self.generator, "_sampling_schema"):
-            if len(self.generator._sampling_schema) == 1:
-                return self.node_model()
-            elif len(self.generator._sampling_schema) == 2:
-                return self.link_model()
-            else:
-                raise RuntimeError(
-                    "The generator used for model creation is neither a node nor a link generator, "
-                    "unable to figure out how to build the model. Consider using node_model or "
-                    "link_model method explicitly to build node or link prediction model, respectively."
-                )
-        else:
-            raise RuntimeError(
-                "Suitable generator is not provided at model creation time, unable to figure out how to build the model. "
-                "Consider either providing a generator, or using node_model or link_model method explicitly to build node or "
-                "link prediction model, respectively."
-            )
+        return self.link_model()
 
     def default_model(self, flatten_output=True):
         warnings.warn(

From 535e39dbcf40325f50087818f2845634c9380104 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:08:11 +1000
Subject: [PATCH 40/82] add attri2vec node classification demo on DBLP

---
 ...vec-DBLP-node-classification-example.ipynb | 749 ++++++++++++++++++
 1 file changed, 749 insertions(+)
 create mode 100644 demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb

diff --git a/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb
new file mode 100644
index 000000000..6c80a71d3
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Node Representation Learning with attri2vec on DBLP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
+    "\n",
+    "\n",
+    "## attri2vec\n",
+    "\n",
+    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
+    "\n",
+    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
+    "\n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "\n",
+    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import stellargraph as sg\n",
+    "from stellargraph.data import UnsupervisedSampler\n",
+    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
+    "from stellargraph.layer import Attri2Vec, link_classification\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading DBLP network data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This demo uses a DBLP citation network, a subgraph extracted from [DBLP-Citation-network V3](https://aminer.org/citation). To form this subgraph, papers from four subjects are extracted according to their venue information: *Database, Data Mining, Artificial Intelligence and Computer Vision*, and papers with no citations are removed. The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word. By ignoring the citation direction, we take the DBLP subgraph as an undirected network.\n",
+    "\n",
+    "The dataset used in this demo can be downloaded from https://www.kaggle.com/daozhang/dblp-subgraph.\n",
+    "The following is the description of the dataset:\n",
+    "\n",
+    "> The content.txt file contains descriptions of the papers in the following format:\n",
+    "\n",
+    " \t\t<paper_id> <word_attributes> <class_label> <publication_year>\n",
+    "        \n",
+    "> The first entry in each line contains the unique integer ID (ranging from 0 to 18,447) of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last two entries in the line are the class label and the publication year of the paper.\n",
+    "> The edgeList.txt file contains the citation relations. Each line describes a link in the following format:\n",
+    "\t\t\n",
+    "        <ID of paper1> <ID of paper2>\n",
+    "        \n",
+    "> Each line contains two paper IDs, with paper2 citing paper1 or paper1 citing paper2.\n",
+    "\n",
+    "\n",
+    "Download and unzip the dblp-subgraph.zip file to a location on your computer and set the `data_dir` variable to\n",
+    "point to the location of the dataset (the \"DBLP\" directory containing \"content.txt\" and \"edgeList.txt\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"~/data/DBLP\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the graph from the edgelist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edgelist = pd.read_csv(os.path.join(data_dir, \"edgeList.txt\"), sep='\\t', header=None, names=[\"source\", \"target\"])\n",
+    "edgelist[\"label\"] = \"cites\"  # set the edge type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load paper content features, subjects and publishing years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_names = [\"w_{}\".format(ii) for ii in range(2476)]\n",
+    "node_column_names =  feature_names + [\"subject\", \"year\"]\n",
+    "node_data = pd.read_csv(os.path.join(data_dir, \"content.txt\"), sep='\\t', header=None, names=node_column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct the graph from edge list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(G_nx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_features = node_data[feature_names]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = sg.StellarGraph(G_nx, node_features=node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 18448, Edges: 45611\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [18448]\n",
+      "    Edge types: paper-cites->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-cites->paper: [45611]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train attri2vec on the DBLP Subgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = list(G.nodes())\n",
+    "number_of_walks = 4\n",
+    "length = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the batch size and the number of epochs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 50\n",
+    "epochs = 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Attri2VecLinkGenerator with an estimated 14758 batches generated on the fly per epoch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
+    "\n",
+    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer_sizes = [128]\n",
+    "attri2vec = Attri2Vec(\n",
+    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0924 14:51:32.580700 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+      "W0924 14:51:32.618021 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
+    "x_inp, x_out = attri2vec.build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = link_classification(\n",
+    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
+    ")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
+    "    loss=keras.losses.binary_crossentropy,\n",
+    "    metrics=[keras.metrics.binary_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0924 14:51:33.217601 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/8\n",
+      "14759/14759 [==============================] - 384s 26ms/step - loss: 0.7016 - binary_accuracy: 0.5404\n",
+      "Epoch 2/8\n",
+      "14759/14759 [==============================] - 397s 27ms/step - loss: 0.6156 - binary_accuracy: 0.6309\n",
+      "Epoch 3/8\n",
+      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.5221 - binary_accuracy: 0.7362\n",
+      "Epoch 4/8\n",
+      "14759/14759 [==============================] - 407s 28ms/step - loss: 0.4527 - binary_accuracy: 0.7909\n",
+      "Epoch 5/8\n",
+      "14759/14759 [==============================] - 389s 26ms/step - loss: 0.4029 - binary_accuracy: 0.8219\n",
+      "Epoch 6/8\n",
+      "14759/14759 [==============================] - 409s 28ms/step - loss: 0.3673 - binary_accuracy: 0.8424\n",
+      "Epoch 7/8\n",
+      "14759/14759 [==============================] - 399s 27ms/step - loss: 0.3387 - binary_accuracy: 0.8573\n",
+      "Epoch 8/8\n",
+      "14759/14759 [==============================] - 387s 26ms/step - loss: 0.3189 - binary_accuracy: 0.8679\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit_generator(\n",
+    "    train_gen,\n",
+    "    epochs=epochs,\n",
+    "    verbose=1,\n",
+    "    use_multiprocessing=False,\n",
+    "    workers=1,\n",
+    "    shuffle=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Visualise Node Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inp_src = x_inp[0]\n",
+    "x_out_src = x_out[0]\n",
+    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node embeddings by applying the learned mapping function to node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "369/369 [==============================] - 1s 2ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_ids = node_data.index\n",
+    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
+    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node subjects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_targets = [ node_data[\"subject\"][node_id] for node_id in node_ids ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transform the embeddings to 2d space for visualisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform = TSNE # PCA\n",
+    "\n",
+    "trans = transform(n_components=2)\n",
+    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw the embedding points, coloring them by the target label (paper subject)\n",
+    "alpha = 0.7\n",
+    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
+    "node_colours = [ label_map[target] for target in node_targets ]\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plt.axes().set(aspect=\"equal\")\n",
+    "plt.scatter(node_embeddings_2d[:,0], \n",
+    "            node_embeddings_2d[:,1], \n",
+    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
+    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Node Classification Task"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The embeddings learned by `attri2vec` can be used as feature vectors in downstream tasks, such as node classification and link prediction.\n",
+    "\n",
+    "In this example, we will use the `attri2vec` node embeddings to train a classifier to predict the subject of a paper in DBLP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X will hold the 128-dimensional input features\n",
+    "X = node_embeddings  \n",
+    "# y holds the corresponding target values\n",
+    "y = np.array(node_targets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Splitting\n",
+    "\n",
+    "We split the data into train and test sets. \n",
+    "\n",
+    "We use 10% of the data for training and the remaining 90% for testing as a hold out test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Array shapes:\n",
+      " X_train = (1844, 128)\n",
+      " y_train = (1844,)\n",
+      " X_test = (16604, 128)\n",
+      " y_test = (16604,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.1, test_size=None)\n",
+    "print(\"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classifier Training\n",
+    "\n",
+    "We train a Logistic Regression classifier on the training data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
+       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
+       "                     max_iter=2000, multi_class='ovr', n_jobs=None,\n",
+       "                     penalty='l2', random_state=None, refit=True,\n",
+       "                     scoring='accuracy', solver='lbfgs', tol=0.0001,\n",
+       "                     verbose=False)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = LogisticRegressionCV(\n",
+    "    Cs=10, \n",
+    "    cv=10, \n",
+    "    scoring=\"accuracy\",\n",
+    "    verbose=False,\n",
+    "    multi_class='ovr',\n",
+    "    max_iter=2000\n",
+    ")\n",
+    "clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict the hold-out test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the accuracy of the classifier on the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8125752830643218"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 1052ff5c1208e48302287fd4afbe9bf45a001a93 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:08:31 +1000
Subject: [PATCH 41/82] add attri2vec node classification demo on citeseer

---
 ...citeseer-node-classification-example.ipynb | 829 ++++++++++++++++++
 1 file changed, 829 insertions(+)
 create mode 100644 demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb

diff --git a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
new file mode 100644
index 000000000..1b02e60ec
--- /dev/null
+++ b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
@@ -0,0 +1,829 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Node Representation Learning with attri2vec on Citeseer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
+    "\n",
+    "\n",
+    "## attri2vec\n",
+    "\n",
+    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
+    "\n",
+    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
+    "\n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "\n",
+    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import stellargraph as sg\n",
+    "from stellargraph.data import UnsupervisedSampler\n",
+    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
+    "from stellargraph.layer import Attri2Vec, link_classification\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "from pandas.core.indexes.base import Index\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset is the citation network Citeseer.\n",
+    "\n",
+    "It can be downloaded by clicking [here](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz)\n",
+    "\n",
+    "The following is the description of the dataset from the publisher,\n",
+    "> The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. The README file in the dataset provides more details.\n",
+    "\n",
+    "Download and unzip the citeseer.tgz file to a location on your computer. \n",
+    "\n",
+    "We assume that the dataset is stored in the directory\n",
+    "\n",
+    "`~/data/citeseer/`\n",
+    "\n",
+    "where the files `citeseer.cites` and `citeseer.content` can be located.\n",
+    "\n",
+    "We are going to load the data into a networkx object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"~/data/citeseer\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load edges in order 'cited-paper' <- 'citing-paper'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "citeseer_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.cites\"))\n",
+    "g_nx = nx.read_edgelist(path=citeseer_location, create_using=nx.DiGraph()).reverse()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert the graph to undirected graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g_nx = g_nx.to_undirected()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the node attribute data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  interactivity=interactivity, compiler=compiler, result=result)\n"
+     ]
+    }
+   ],
+   "source": [
+    "citeseer_data_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.content\"))\n",
+    "attr_names = [\"w_{}\".format(ii) for ii in range(3703)]\n",
+    "node_column_names =  attr_names + [\"subject\"]\n",
+    "node_attr = pd.read_csv(citeseer_data_location, sep='\\t', header=None, names=node_column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change the type of the indexes of node_attr to str."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_attr.index = Index(list(map(str, list(node_attr.index))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original graph contains some nodes with no attributes. We remove them here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g_nx = g_nx.subgraph(list(node_attr.index))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the largest connected component. For clarity we ignore isolated nodes and subgraphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Largest subgraph statistics: 2110 nodes, 3720 edges\n"
+     ]
+    }
+   ],
+   "source": [
+    "g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
+    "g_nx = max(g_nx_ccs, key=len)\n",
+    "print(\"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
+    "    g_nx.number_of_nodes(), g_nx.number_of_edges()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(g_nx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the ids of the nodes in the selected largest connected component. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_ids = sorted(list(g_nx.nodes))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_features = node_attr[attr_names].reindex(node_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = sg.StellarGraph(g_nx, node_features=node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 2110, Edges: 3720\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [2110]\n",
+      "    Edge types: paper-default->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-default->paper: [3720]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train attri2vec on Citeseer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = list(G.nodes())\n",
+    "number_of_walks = 4\n",
+    "length = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the batch size and the number of epochs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 50\n",
+    "epochs = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Attri2VecLinkGenerator with an estimated 1688 batches generated on the fly per epoch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
+    "\n",
+    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer_sizes = [128]\n",
+    "attri2vec = Attri2Vec(\n",
+    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0925 10:19:03.073729 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+      "W0925 10:19:03.096633 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
+    "x_inp, x_out = attri2vec.build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = link_classification(\n",
+    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
+    ")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
+    "    loss=keras.losses.binary_crossentropy,\n",
+    "    metrics=[keras.metrics.binary_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0925 10:19:03.339447 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1688/1688 [==============================] - 12s 7ms/step - loss: 0.6820 - binary_accuracy: 0.5580\n",
+      "Epoch 2/4\n",
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.5126 - binary_accuracy: 0.7584\n",
+      "Epoch 3/4\n",
+      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3356 - binary_accuracy: 0.8780\n",
+      "Epoch 4/4\n",
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2456 - binary_accuracy: 0.9161\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit_generator(\n",
+    "    train_gen,\n",
+    "    epochs=epochs,\n",
+    "    verbose=1,\n",
+    "    use_multiprocessing=False,\n",
+    "    workers=1,\n",
+    "    shuffle=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Visualise Node Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inp_src = x_inp[0]\n",
+    "x_out_src = x_out[0]\n",
+    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node embeddings by applying the learned mapping function to node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "43/43 [==============================] - 0s 2ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
+    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node subjects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_targets = [ node_attr[\"subject\"][node_id] for node_id in node_ids ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transform the embeddings to 2d space for visualisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform = TSNE # PCA\n",
+    "\n",
+    "trans = transform(n_components=2)\n",
+    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw the embedding points, coloring them by the target label (paper subject)\n",
+    "alpha = 0.7\n",
+    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
+    "node_colours = [ label_map[target] for target in node_targets ]\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plt.axes().set(aspect=\"equal\")\n",
+    "plt.scatter(node_embeddings_2d[:,0], \n",
+    "            node_embeddings_2d[:,1], \n",
+    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
+    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Node Classificaion Task"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The embeddings learned by `attri2vec` can be used as feature vectors in downstream tasks, such as node classification and link prediction.\n",
+    "\n",
+    "In this example, we will use the `attri2vec` node embeddings to train a classifier to predict the subject of a paper in DBLP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X will hold the 128-dimensional input features\n",
+    "X = node_embeddings  \n",
+    "# y holds the corresponding target values\n",
+    "y = np.array(node_targets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Splitting\n",
+    "\n",
+    "We split the data into train and test sets. \n",
+    "\n",
+    "We use 10% of the data for training and the remaining 90% for testing as a hold out test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Array shapes:\n",
+      " X_train = (211, 128)\n",
+      " y_train = (211,)\n",
+      " X_test = (1899, 128)\n",
+      " y_test = (1899,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.1, test_size=None)\n",
+    "print(\"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classifier Training\n",
+    "\n",
+    "We train a Logistic Regression classifier on the training data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
+       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
+       "                     max_iter=300, multi_class='ovr', n_jobs=None, penalty='l2',\n",
+       "                     random_state=None, refit=True, scoring='accuracy',\n",
+       "                     solver='lbfgs', tol=0.0001, verbose=False)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = LogisticRegressionCV(\n",
+    "    Cs=10, \n",
+    "    cv=10, \n",
+    "    scoring=\"accuracy\",\n",
+    "    verbose=False,\n",
+    "    multi_class='ovr',\n",
+    "    max_iter=300\n",
+    ")\n",
+    "clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict the hold-out test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the accuracy of the classifier on the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7588204318062138"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From bcf934d10ad52af646495733b0c8821ffbb0c20e Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:36:29 +1000
Subject: [PATCH 42/82] Delete
 attri2vec-citeseer-node-classification-example.ipynb

---
 ...citeseer-node-classification-example.ipynb | 829 ------------------
 1 file changed, 829 deletions(-)
 delete mode 100644 demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb

diff --git a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
deleted file mode 100644
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--- a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
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@@ -1,829 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Node Representation Learning with attri2vec on Citeseer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
-    "\n",
-    "\n",
-    "## attri2vec\n",
-    "\n",
-    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
-    "\n",
-    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
-    "\n",
-    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
-    "\n",
-    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "import stellargraph as sg\n",
-    "from stellargraph.data import UnsupervisedSampler\n",
-    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
-    "from stellargraph.layer import Attri2Vec, link_classification\n",
-    "\n",
-    "from tensorflow import keras\n",
-    "\n",
-    "from pandas.core.indexes.base import Index\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
-    "from sklearn.metrics import accuracy_score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The dataset is the citation network Citeseer.\n",
-    "\n",
-    "It can be downloaded by clicking [here](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz)\n",
-    "\n",
-    "The following is the description of the dataset from the publisher,\n",
-    "> The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. The README file in the dataset provides more details.\n",
-    "\n",
-    "Download and unzip the citeseer.tgz file to a location on your computer. \n",
-    "\n",
-    "We assume that the dataset is stored in the directory\n",
-    "\n",
-    "`~/data/citeseer/`\n",
-    "\n",
-    "where the files `citeseer.cites` and `citeseer.content` can be located.\n",
-    "\n",
-    "We are going to load the data into a networkx object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dir = \"~/data/citeseer\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load edges in order 'cited-paper' <- 'citing-paper'."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "citeseer_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.cites\"))\n",
-    "g_nx = nx.read_edgelist(path=citeseer_location, create_using=nx.DiGraph()).reverse()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Convert the graph to undirected graph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g_nx = g_nx.to_undirected()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load the node attribute data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "citeseer_data_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.content\"))\n",
-    "attr_names = [\"w_{}\".format(ii) for ii in range(3703)]\n",
-    "node_column_names =  attr_names + [\"subject\"]\n",
-    "node_attr = pd.read_csv(citeseer_data_location, sep='\\t', header=None, names=node_column_names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Change the type of the indexes of node_attr to str."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_attr.index = Index(list(map(str, list(node_attr.index))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The original graph contains some nodes with no attributes. We remove them here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g_nx = g_nx.subgraph(list(node_attr.index))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select the largest connected component. For clarity we ignore isolated nodes and subgraphs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Largest subgraph statistics: 2110 nodes, 3720 edges\n"
-     ]
-    }
-   ],
-   "source": [
-    "g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
-    "g_nx = max(g_nx_ccs, key=len)\n",
-    "print(\"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
-    "    g_nx.number_of_nodes(), g_nx.number_of_edges()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify node types."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nx.set_node_attributes(g_nx, \"paper\", \"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the ids of the nodes in the selected largest connected component. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_ids = sorted(list(g_nx.nodes))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_features = node_attr[attr_names].reindex(node_ids)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the Stellargraph with node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = sg.StellarGraph(g_nx, node_features=node_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 2110, Edges: 3720\n",
-      "\n",
-      " Node types:\n",
-      "  paper: [2110]\n",
-      "    Edge types: paper-default->paper\n",
-      "\n",
-      " Edge types:\n",
-      "    paper-default->paper: [3720]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(G.info())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train attri2vec on Citeseer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes = list(G.nodes())\n",
-    "number_of_walks = 4\n",
-    "length = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set the batch size and the number of epochs. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 50\n",
-    "epochs = 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Attri2VecLinkGenerator with an estimated 1688 batches generated on the fly per epoch.\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
-    "\n",
-    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "layer_sizes = [128]\n",
-    "attri2vec = Attri2Vec(\n",
-    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 10:19:03.073729 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 10:19:03.096633 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
-    "x_inp, x_out = attri2vec.build()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
-     ]
-    }
-   ],
-   "source": [
-    "prediction = link_classification(\n",
-    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
-    ")(x_out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
-    "    loss=keras.losses.binary_crossentropy,\n",
-    "    metrics=[keras.metrics.binary_accuracy],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Train the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/4\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "W0925 10:19:03.339447 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1688/1688 [==============================] - 12s 7ms/step - loss: 0.6820 - binary_accuracy: 0.5580\n",
-      "Epoch 2/4\n",
-      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.5126 - binary_accuracy: 0.7584\n",
-      "Epoch 3/4\n",
-      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3356 - binary_accuracy: 0.8780\n",
-      "Epoch 4/4\n",
-      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2456 - binary_accuracy: 0.9161\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit_generator(\n",
-    "    train_gen,\n",
-    "    epochs=epochs,\n",
-    "    verbose=1,\n",
-    "    use_multiprocessing=False,\n",
-    "    workers=1,\n",
-    "    shuffle=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Visualise Node Embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_inp_src = x_inp[0]\n",
-    "x_out_src = x_out[0]\n",
-    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the node embeddings by applying the learned mapping function to node content features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "43/43 [==============================] - 0s 2ms/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
-    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get node subjects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_targets = [ node_attr[\"subject\"][node_id] for node_id in node_ids ]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Transform the embeddings to 2d space for visualisation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform = TSNE # PCA\n",
-    "\n",
-    "trans = transform(n_components=2)\n",
-    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# draw the embedding points, coloring them by the target label (paper subject)\n",
-    "alpha = 0.7\n",
-    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
-    "node_colours = [ label_map[target] for target in node_targets ]\n",
-    "\n",
-    "plt.figure(figsize=(7,7))\n",
-    "plt.axes().set(aspect=\"equal\")\n",
-    "plt.scatter(node_embeddings_2d[:,0], \n",
-    "            node_embeddings_2d[:,1], \n",
-    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
-    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Node Classificaion Task"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The embeddings learned by `attri2vec` can be used as feature vectors in downstream tasks, such as node classification and link prediction.\n",
-    "\n",
-    "In this example, we will use the `attri2vec` node embeddings to train a classifier to predict the subject of a paper in DBLP."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# X will hold the 128-dimensional input features\n",
-    "X = node_embeddings  \n",
-    "# y holds the corresponding target values\n",
-    "y = np.array(node_targets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Splitting\n",
-    "\n",
-    "We split the data into train and test sets. \n",
-    "\n",
-    "We use 10% of the data for training and the remaining 90% for testing as a hold out test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Array shapes:\n",
-      " X_train = (211, 128)\n",
-      " y_train = (211,)\n",
-      " X_test = (1899, 128)\n",
-      " y_test = (1899,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.1, test_size=None)\n",
-    "print(\"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Classifier Training\n",
-    "\n",
-    "We train a Logistic Regression classifier on the training data. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
-       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
-       "                     max_iter=300, multi_class='ovr', n_jobs=None, penalty='l2',\n",
-       "                     random_state=None, refit=True, scoring='accuracy',\n",
-       "                     solver='lbfgs', tol=0.0001, verbose=False)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clf = LogisticRegressionCV(\n",
-    "    Cs=10, \n",
-    "    cv=10, \n",
-    "    scoring=\"accuracy\",\n",
-    "    verbose=False,\n",
-    "    multi_class='ovr',\n",
-    "    max_iter=300\n",
-    ")\n",
-    "clf.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Predict the hold-out test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = clf.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculate the accuracy of the classifier on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7588204318062138"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(y_test, y_pred)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From cc6d00105fecd776fc827a69e4bd719daa198d35 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:44:32 +1000
Subject: [PATCH 43/82] add attri2vec node classification demo on citeseer

---
 ...citeseer-node-classification-example.ipynb | 829 ++++++++++++++++++
 1 file changed, 829 insertions(+)
 create mode 100644 demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb

diff --git a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
new file mode 100644
index 000000000..1b02e60ec
--- /dev/null
+++ b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
@@ -0,0 +1,829 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Node Representation Learning with attri2vec on Citeseer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
+    "\n",
+    "\n",
+    "## attri2vec\n",
+    "\n",
+    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
+    "\n",
+    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
+    "\n",
+    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
+    "\n",
+    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import stellargraph as sg\n",
+    "from stellargraph.data import UnsupervisedSampler\n",
+    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
+    "from stellargraph.layer import Attri2Vec, link_classification\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "from pandas.core.indexes.base import Index\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset is the citation network Citeseer.\n",
+    "\n",
+    "It can be downloaded by clicking [here](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz)\n",
+    "\n",
+    "The following is the description of the dataset from the publisher,\n",
+    "> The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. The README file in the dataset provides more details.\n",
+    "\n",
+    "Download and unzip the citeseer.tgz file to a location on your computer. \n",
+    "\n",
+    "We assume that the dataset is stored in the directory\n",
+    "\n",
+    "`~/data/citeseer/`\n",
+    "\n",
+    "where the files `citeseer.cites` and `citeseer.content` can be located.\n",
+    "\n",
+    "We are going to load the data into a networkx object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"~/data/citeseer\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load edges in order 'cited-paper' <- 'citing-paper'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "citeseer_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.cites\"))\n",
+    "g_nx = nx.read_edgelist(path=citeseer_location, create_using=nx.DiGraph()).reverse()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert the graph to undirected graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g_nx = g_nx.to_undirected()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the node attribute data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  interactivity=interactivity, compiler=compiler, result=result)\n"
+     ]
+    }
+   ],
+   "source": [
+    "citeseer_data_location = os.path.expanduser(os.path.join(data_dir, \"citeseer.content\"))\n",
+    "attr_names = [\"w_{}\".format(ii) for ii in range(3703)]\n",
+    "node_column_names =  attr_names + [\"subject\"]\n",
+    "node_attr = pd.read_csv(citeseer_data_location, sep='\\t', header=None, names=node_column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change the type of the indexes of node_attr to str."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_attr.index = Index(list(map(str, list(node_attr.index))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original graph contains some nodes with no attributes. We remove them here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g_nx = g_nx.subgraph(list(node_attr.index))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the largest connected component. For clarity we ignore isolated nodes and subgraphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Largest subgraph statistics: 2110 nodes, 3720 edges\n"
+     ]
+    }
+   ],
+   "source": [
+    "g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
+    "g_nx = max(g_nx_ccs, key=len)\n",
+    "print(\"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
+    "    g_nx.number_of_nodes(), g_nx.number_of_edges()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify node types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx.set_node_attributes(g_nx, \"paper\", \"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the ids of the nodes in the selected largest connected component. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_ids = sorted(list(g_nx.nodes))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_features = node_attr[attr_names].reindex(node_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Stellargraph with node features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = sg.StellarGraph(g_nx, node_features=node_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 2110, Edges: 3720\n",
+      "\n",
+      " Node types:\n",
+      "  paper: [2110]\n",
+      "    Edge types: paper-default->paper\n",
+      "\n",
+      " Edge types:\n",
+      "    paper-default->paper: [3720]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(G.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train attri2vec on Citeseer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = list(G.nodes())\n",
+    "number_of_walks = 4\n",
+    "length = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the batch size and the number of epochs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 50\n",
+    "epochs = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Attri2VecLinkGenerator with an estimated 1688 batches generated on the fly per epoch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
+    "\n",
+    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer_sizes = [128]\n",
+    "attri2vec = Attri2Vec(\n",
+    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0925 10:19:03.073729 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+      "W0925 10:19:03.096633 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
+    "x_inp, x_out = attri2vec.build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = link_classification(\n",
+    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
+    ")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
+    "    loss=keras.losses.binary_crossentropy,\n",
+    "    metrics=[keras.metrics.binary_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W0925 10:19:03.339447 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1688/1688 [==============================] - 12s 7ms/step - loss: 0.6820 - binary_accuracy: 0.5580\n",
+      "Epoch 2/4\n",
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.5126 - binary_accuracy: 0.7584\n",
+      "Epoch 3/4\n",
+      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3356 - binary_accuracy: 0.8780\n",
+      "Epoch 4/4\n",
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2456 - binary_accuracy: 0.9161\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit_generator(\n",
+    "    train_gen,\n",
+    "    epochs=epochs,\n",
+    "    verbose=1,\n",
+    "    use_multiprocessing=False,\n",
+    "    workers=1,\n",
+    "    shuffle=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Visualise Node Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inp_src = x_inp[0]\n",
+    "x_out_src = x_out[0]\n",
+    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the node embeddings by applying the learned mapping function to node content features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "43/43 [==============================] - 0s 2ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
+    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get node subjects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_targets = [ node_attr[\"subject\"][node_id] for node_id in node_ids ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transform the embeddings to 2d space for visualisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform = TSNE # PCA\n",
+    "\n",
+    "trans = transform(n_components=2)\n",
+    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw the embedding points, coloring them by the target label (paper subject)\n",
+    "alpha = 0.7\n",
+    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
+    "node_colours = [ label_map[target] for target in node_targets ]\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plt.axes().set(aspect=\"equal\")\n",
+    "plt.scatter(node_embeddings_2d[:,0], \n",
+    "            node_embeddings_2d[:,1], \n",
+    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
+    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Node Classificaion Task"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The embeddings learned by `attri2vec` can be used as feature vectors in downstream tasks, such as node classification and link prediction.\n",
+    "\n",
+    "In this example, we will use the `attri2vec` node embeddings to train a classifier to predict the subject of a paper in DBLP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X will hold the 128-dimensional input features\n",
+    "X = node_embeddings  \n",
+    "# y holds the corresponding target values\n",
+    "y = np.array(node_targets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Splitting\n",
+    "\n",
+    "We split the data into train and test sets. \n",
+    "\n",
+    "We use 10% of the data for training and the remaining 90% for testing as a hold out test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Array shapes:\n",
+      " X_train = (211, 128)\n",
+      " y_train = (211,)\n",
+      " X_test = (1899, 128)\n",
+      " y_test = (1899,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.1, test_size=None)\n",
+    "print(\"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classifier Training\n",
+    "\n",
+    "We train a Logistic Regression classifier on the training data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
+       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
+       "                     max_iter=300, multi_class='ovr', n_jobs=None, penalty='l2',\n",
+       "                     random_state=None, refit=True, scoring='accuracy',\n",
+       "                     solver='lbfgs', tol=0.0001, verbose=False)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = LogisticRegressionCV(\n",
+    "    Cs=10, \n",
+    "    cv=10, \n",
+    "    scoring=\"accuracy\",\n",
+    "    verbose=False,\n",
+    "    multi_class='ovr',\n",
+    "    max_iter=300\n",
+    ")\n",
+    "clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict the hold-out test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the accuracy of the classifier on the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7588204318062138"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From ed4dd445787fb55554b5bfcc0492fe196edf503a Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 11:47:08 +1000
Subject: [PATCH 44/82] update demos/node-classification/attri2vec/README.md

---
 demos/node-classification/attri2vec/README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/demos/node-classification/attri2vec/README.md b/demos/node-classification/attri2vec/README.md
index 38bab7ac9..89355cc18 100644
--- a/demos/node-classification/attri2vec/README.md
+++ b/demos/node-classification/attri2vec/README.md
@@ -1,7 +1,7 @@
 ## Node classification using attri2vec [1]
 
 This folder contains two [Jupyter](http://jupyter.org/) python notebooks demonstrating the combined use of
-`stellargraph` (this library), `Scikit-learn` [3] libraries for node classification in two homogeneous graphs
+`stellargraph` (this library), `Scikit-learn` [2] libraries for node classification in two homogeneous graphs
 attached with node attributes.
 
 The first example demonstrates node representation learning and node classification using the citeseer
@@ -61,3 +61,5 @@ and pass this location as a command line argument to this script.
 
 **1.** Attributed Network Embedding via Subspace Discovery. D. Zhang, J, Yin, X. Zhu and C. Zhang, arXiv:1901.04095,
 [cs.SI], 2019. ([link](https://arxiv.org/abs/1901.04095))
+
+**2.** Scikit-learn: Machine learning in Python ([link](http://scikit-learn.org/stable/))

From 4e943dbd8a08ca71fc4ef5dfebb766a8f3b042cc Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:02:09 +1000
Subject: [PATCH 45/82] rerun attri2vec out-of-sample link prediction demo on
 DBLP

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 34 +++++++------------
 1 file changed, 13 insertions(+), 21 deletions(-)

diff --git a/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb b/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
index 984cbb562..17e8cfa88 100644
--- a/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
+++ b/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
@@ -30,15 +30,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import networkx as nx\n",
     "import pandas as pd\n",
@@ -478,10 +470,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 10:21:27.091693 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 17:46:06.513718 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 10:21:27.113364 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 17:46:06.534775 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -558,7 +550,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0925 10:21:27.574651 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0925 17:46:07.069445 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -568,17 +560,17 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/6\n",
-      "4711/4711 [==============================] - 72s 15ms/step - loss: 0.7680 - binary_accuracy: 0.5814\n",
+      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.7018 - binary_accuracy: 0.6224\n",
       "Epoch 2/6\n",
-      "4711/4711 [==============================] - 72s 15ms/step - loss: 0.6447 - binary_accuracy: 0.6101\n",
+      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.6562 - binary_accuracy: 0.6042\n",
       "Epoch 3/6\n",
-      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.5866 - binary_accuracy: 0.6483\n",
+      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.5475 - binary_accuracy: 0.6931\n",
       "Epoch 4/6\n",
-      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.4925 - binary_accuracy: 0.7284\n",
+      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.4017 - binary_accuracy: 0.8048\n",
       "Epoch 5/6\n",
-      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.3574 - binary_accuracy: 0.8300\n",
+      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.2637 - binary_accuracy: 0.8900\n",
       "Epoch 6/6\n",
-      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.2242 - binary_accuracy: 0.9105\n"
+      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.1570 - binary_accuracy: 0.9473\n"
      ]
     }
    ],
@@ -634,7 +626,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "369/369 [==============================] - 1s 1ms/step\n"
+      "369/369 [==============================] - 1s 2ms/step\n"
      ]
     }
    ],
@@ -778,7 +770,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7842243817182476"
+       "0.8085538458796013"
       ]
      },
      "execution_count": 33,
@@ -889,7 +881,7 @@
     {
      "data": {
       "text/plain": [
-       "0.6666971120688185"
+       "0.6651603215420048"
       ]
      },
      "execution_count": 38,

From 80a016154a1d1f5d00aa069a7100e5f884cd534b Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:02:34 +1000
Subject: [PATCH 46/82] rerun the attri2vec DBLP embedding demo

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 42 +++++++++----------
 1 file changed, 20 insertions(+), 22 deletions(-)

diff --git a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
index 5b9148316..b49475e58 100644
--- a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
+++ b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
@@ -29,15 +29,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import networkx as nx\n",
     "import pandas as pd\n",
@@ -340,10 +332,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0924 14:51:32.580700 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 16:19:56.064584 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0924 14:51:32.618021 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 16:19:56.085483 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -416,11 +408,18 @@
     "scrolled": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/8\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0924 14:51:33.217601 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0925 16:19:56.485485 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -429,22 +428,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/8\n",
-      "14759/14759 [==============================] - 384s 26ms/step - loss: 0.7016 - binary_accuracy: 0.5404\n",
+      "14759/14759 [==============================] - 371s 25ms/step - loss: 0.7013 - binary_accuracy: 0.5400\n",
       "Epoch 2/8\n",
-      "14759/14759 [==============================] - 397s 27ms/step - loss: 0.6156 - binary_accuracy: 0.6309\n",
+      "14759/14759 [==============================] - 373s 25ms/step - loss: 0.6139 - binary_accuracy: 0.6296\n",
       "Epoch 3/8\n",
-      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.5221 - binary_accuracy: 0.7362\n",
+      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.5194 - binary_accuracy: 0.7376\n",
       "Epoch 4/8\n",
-      "14759/14759 [==============================] - 407s 28ms/step - loss: 0.4527 - binary_accuracy: 0.7909\n",
+      "14759/14759 [==============================] - 376s 25ms/step - loss: 0.4491 - binary_accuracy: 0.7930\n",
       "Epoch 5/8\n",
-      "14759/14759 [==============================] - 389s 26ms/step - loss: 0.4029 - binary_accuracy: 0.8219\n",
+      "14759/14759 [==============================] - 378s 26ms/step - loss: 0.4003 - binary_accuracy: 0.8237\n",
       "Epoch 6/8\n",
-      "14759/14759 [==============================] - 409s 28ms/step - loss: 0.3673 - binary_accuracy: 0.8424\n",
+      "14759/14759 [==============================] - 382s 26ms/step - loss: 0.3669 - binary_accuracy: 0.8434\n",
       "Epoch 7/8\n",
-      "14759/14759 [==============================] - 399s 27ms/step - loss: 0.3387 - binary_accuracy: 0.8573\n",
+      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.3407 - binary_accuracy: 0.8566\n",
       "Epoch 8/8\n",
-      "14759/14759 [==============================] - 387s 26ms/step - loss: 0.3189 - binary_accuracy: 0.8679\n"
+      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.3204 - binary_accuracy: 0.8673\n"
      ]
     }
    ],
@@ -552,7 +550,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]

From 02a8bb15a6dacab3d1f4d4a23b6aae5abdbe195f Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:02:54 +1000
Subject: [PATCH 47/82] rerun attri2vec citeseer embedding demo

---
 .../stellargraph-attri2vec-citeseer.ipynb     | 36 ++++++-------------
 1 file changed, 11 insertions(+), 25 deletions(-)

diff --git a/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb b/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
index 41f6b60ad..ada0dbbcc 100644
--- a/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
+++ b/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
@@ -29,15 +29,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import networkx as nx\n",
     "import pandas as pd\n",
@@ -424,10 +416,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 10:19:03.073729 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 17:39:33.838998 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 10:19:03.096633 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 17:39:33.886423 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -500,18 +492,11 @@
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/4\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0925 10:19:03.339447 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0925 17:39:34.398716 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -520,13 +505,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1688/1688 [==============================] - 12s 7ms/step - loss: 0.6820 - binary_accuracy: 0.5580\n",
+      "Epoch 1/4\n",
+      "1688/1688 [==============================] - 20s 12ms/step - loss: 0.6833 - binary_accuracy: 0.5538\n",
       "Epoch 2/4\n",
-      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.5126 - binary_accuracy: 0.7584\n",
+      "1688/1688 [==============================] - 18s 11ms/step - loss: 0.5183 - binary_accuracy: 0.7489\n",
       "Epoch 3/4\n",
-      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3356 - binary_accuracy: 0.8780\n",
+      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.3391 - binary_accuracy: 0.8740\n",
       "Epoch 4/4\n",
-      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2456 - binary_accuracy: 0.9161\n"
+      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.2485 - binary_accuracy: 0.9145\n"
      ]
     }
    ],
@@ -582,7 +568,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "43/43 [==============================] - 0s 2ms/step\n"
+      "43/43 [==============================] - 0s 3ms/step\n"
      ]
     }
    ],
@@ -633,7 +619,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]

From 280dacad37694d83fec088aac56a748610e2c035 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:03:10 +1000
Subject: [PATCH 48/82] handle the conflict on node_mappers.py

---
 stellargraph/mapper/node_mappers.py | 35 ++++++++++++++++-------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py
index 1cb442c5c..9beeda0ab 100755
--- a/stellargraph/mapper/node_mappers.py
+++ b/stellargraph/mapper/node_mappers.py
@@ -37,7 +37,6 @@
 import networkx as nx
 import scipy.sparse as sps
 from tensorflow.keras import backend as K
-
 from functools import reduce
 from tensorflow.keras.utils import Sequence
 
@@ -58,11 +57,11 @@ class NodeSequence(Sequence):
 
     This class generated data samples for node inference models
     and should be created using the `.flow(...)` method of
-    :class:`GraphSAGENodeGenerator` or :class:`HinSAGENodeGenerator` 
-    or :class:`Attri2vecNodeGenerator`.
+    :class:`GraphSAGENodeGenerator` or :class:`DirectedGraphSAGENodeGenerator` 
+    or :class:`HinSAGENodeGenerator` or :class:`Attri2VecNodeGenerator`.
 
-    GraphSAGENodeGenerator and HinSAGENodeGenerator are classes that 
-    capture the graph structure and the feature vectors of each node. 
+    GraphSAGENodeGenerator, DirectedGraphSAGENodeGenerator,and HinSAGENodeGenerator 
+    are classes that capture the graph structure and the feature vectors of each node. 
     These generator classes are used within the NodeSequence to generate
     samples of k-hop neighbourhoods in the graph and to return to this 
     class the features from the sampled neighbourhoods.
@@ -71,9 +70,9 @@ class the features from the sampled neighbourhoods.
     of each node.
 
     Args:
-        generator: GraphSAGENodeGenerator or HinSAGENodeGenerator or
-            Attri2VecNodeGenerator. The generator object containing the 
-            graph information.
+        generator: GraphSAGENodeGenerator, DirectedGraphSAGENodeGenerator or 
+            HinSAGENodeGenerator or Attri2VecNodeGenerator. The generator object 
+            containing the graph information.
         ids: list
             A list of the node_ids to be used as head-nodes in the
             downstream task.
@@ -108,20 +107,22 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
                 "Head nodes supplied to generator contain IDs not found in graph"
             )
 
+        # Store the generator to draw samples from graph
         if (
             isinstance(generator, GraphSAGENodeGenerator)
+            or isinstance(generator, DirectedGraphSAGENodeGenerator)
             or isinstance(generator, HinSAGENodeGenerator)
             or isinstance(generator, Attri2VecNodeGenerator)
         ):
             self.generator = generator
         else:
             raise TypeError(
-                "({}) GraphSAGENodeGenerator, HinSAGENodeGenerator or Attri2VecNodeGenerator is required.".format(
+                "({}) GraphSAGENodeGenerator, DirectedGraphSAGENodeGenerator, HinSAGENodeGenerator or Attri2VecNodeGenerator is required.".format(
                     type(self).__name__
                 )
             )
 
-        # Store the generator to draw samples from graph
+        
         self.ids = list(ids)
         self.data_size = len(self.ids)
         self.shuffle = shuffle
@@ -129,8 +130,10 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
         # Shuffle IDs to start
         self.on_epoch_end()
 
-        if isinstance(self.generator, GraphSAGENodeGenerator) or isinstance(
-            self.generator, HinSAGENodeGenerator
+        if (
+            isinstance(self.generator, GraphSAGENodeGenerator) 
+            or isinstance(self.generator, DirectedGraphSAGENodeGenerator)
+            or isinstance(self.generator, HinSAGENodeGenerator)
         ):
             # Infer head_node_type
             if (
@@ -148,12 +151,12 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
 
             # Save head node type and generate sampling schema
             self.head_node_types = [head_node_type]
+            ### Experimental; for directed sampling
+            num_samples = getattr(generator, "num_samples", [])
             self._sampling_schema = generator.schema.sampling_layout(
-                self.head_node_types, generator.num_samples
+                self.head_node_types, num_samples
             )
-	if isinstance(self.generator, GraphSAGENodeGenerator):
-        	### Experimental; for directed sampling
-        	num_samples = getattr(generator, "num_samples", [])
+            
 
     def __len__(self):
         """Denotes the number of batches per epoch"""

From 1c5f819d172a879e4718cf2590d0d00c9a362776 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:03:26 +1000
Subject: [PATCH 49/82] handle the conflicts on link_mappers.py

---
 stellargraph/mapper/link_mappers.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
index 221e13881..3250b941f 100755
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -210,6 +210,8 @@ class OnDemandLinkSequence(Sequence):
     """
 
     def __init__(self, generator, walker):
+        
+        self.lock = threading.Lock()
 
         if isinstance(generator, GraphSAGELinkGenerator) or isinstance(
             generator, Attri2VecLinkGenerator
@@ -221,14 +223,11 @@ def __init__(self, generator, walker):
                     type(self).__name__
                 )
             )
-        self.lock = threading.Lock()
-
-        #self.generator = generator  # graphlinkgenerator instance
-
-        self.head_node_types = self.generator.schema.node_types * 2
 
         if isinstance(self.generator, GraphSAGELinkGenerator):
+            
             self.head_node_types = self.generator.schema.node_types * 2
+            
             # YT: we need to have self._sampling_schema for GraphSAGE.build() method to work
             self._sampling_schema = generator.schema.sampling_layout(
                 self.head_node_types, generator.num_samples
@@ -292,6 +291,7 @@ def __getitem__(self, batch_num):
         self.lock.release()
 
         if isinstance(self.generator, GraphSAGELinkGenerator):
+            
             if self.head_node_types is None:
 
                 # Get head node types from all src, dst nodes extracted from all links,

From 9b30169cc0cd99988da9148da81603c05efd7c4f Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:03:48 +1000
Subject: [PATCH 50/82] rerun the attri2vec DBLP node classification demo

---
 ...vec-DBLP-node-classification-example.ipynb | 44 +++++++++----------
 1 file changed, 21 insertions(+), 23 deletions(-)

diff --git a/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb
index 6c80a71d3..82df33956 100644
--- a/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb
+++ b/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb
@@ -29,15 +29,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import networkx as nx\n",
     "import pandas as pd\n",
@@ -340,10 +332,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0924 14:51:32.580700 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 16:19:56.064584 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0924 14:51:32.618021 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 16:19:56.085483 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -416,11 +408,18 @@
     "scrolled": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/8\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0924 14:51:33.217601 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0925 16:19:56.485485 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -429,22 +428,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/8\n",
-      "14759/14759 [==============================] - 384s 26ms/step - loss: 0.7016 - binary_accuracy: 0.5404\n",
+      "14759/14759 [==============================] - 371s 25ms/step - loss: 0.7013 - binary_accuracy: 0.5400\n",
       "Epoch 2/8\n",
-      "14759/14759 [==============================] - 397s 27ms/step - loss: 0.6156 - binary_accuracy: 0.6309\n",
+      "14759/14759 [==============================] - 373s 25ms/step - loss: 0.6139 - binary_accuracy: 0.6296\n",
       "Epoch 3/8\n",
-      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.5221 - binary_accuracy: 0.7362\n",
+      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.5194 - binary_accuracy: 0.7376\n",
       "Epoch 4/8\n",
-      "14759/14759 [==============================] - 407s 28ms/step - loss: 0.4527 - binary_accuracy: 0.7909\n",
+      "14759/14759 [==============================] - 376s 25ms/step - loss: 0.4491 - binary_accuracy: 0.7930\n",
       "Epoch 5/8\n",
-      "14759/14759 [==============================] - 389s 26ms/step - loss: 0.4029 - binary_accuracy: 0.8219\n",
+      "14759/14759 [==============================] - 378s 26ms/step - loss: 0.4003 - binary_accuracy: 0.8237\n",
       "Epoch 6/8\n",
-      "14759/14759 [==============================] - 409s 28ms/step - loss: 0.3673 - binary_accuracy: 0.8424\n",
+      "14759/14759 [==============================] - 382s 26ms/step - loss: 0.3669 - binary_accuracy: 0.8434\n",
       "Epoch 7/8\n",
-      "14759/14759 [==============================] - 399s 27ms/step - loss: 0.3387 - binary_accuracy: 0.8573\n",
+      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.3407 - binary_accuracy: 0.8566\n",
       "Epoch 8/8\n",
-      "14759/14759 [==============================] - 387s 26ms/step - loss: 0.3189 - binary_accuracy: 0.8679\n"
+      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.3204 - binary_accuracy: 0.8673\n"
      ]
     }
    ],
@@ -552,7 +550,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -712,7 +710,7 @@
     {
      "data": {
       "text/plain": [
-       "0.8125752830643218"
+       "0.8048662972777644"
       ]
      },
      "execution_count": 28,

From e09fab207ec1e2079ebb66aad63c0b63b6c18804 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:04:07 +1000
Subject: [PATCH 51/82] rerun the attri2vec citeseer node classification demo

---
 ...citeseer-node-classification-example.ipynb | 38 ++++++-------------
 1 file changed, 12 insertions(+), 26 deletions(-)

diff --git a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
index 1b02e60ec..ccdf2853a 100644
--- a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
+++ b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
@@ -29,15 +29,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import networkx as nx\n",
     "import pandas as pd\n",
@@ -424,10 +416,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 10:19:03.073729 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 17:39:33.838998 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 10:19:03.096633 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W0925 17:39:33.886423 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -500,18 +492,11 @@
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/4\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0925 10:19:03.339447 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W0925 17:39:34.398716 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -520,13 +505,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1688/1688 [==============================] - 12s 7ms/step - loss: 0.6820 - binary_accuracy: 0.5580\n",
+      "Epoch 1/4\n",
+      "1688/1688 [==============================] - 20s 12ms/step - loss: 0.6833 - binary_accuracy: 0.5538\n",
       "Epoch 2/4\n",
-      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.5126 - binary_accuracy: 0.7584\n",
+      "1688/1688 [==============================] - 18s 11ms/step - loss: 0.5183 - binary_accuracy: 0.7489\n",
       "Epoch 3/4\n",
-      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3356 - binary_accuracy: 0.8780\n",
+      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.3391 - binary_accuracy: 0.8740\n",
       "Epoch 4/4\n",
-      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2456 - binary_accuracy: 0.9161\n"
+      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.2485 - binary_accuracy: 0.9145\n"
      ]
     }
    ],
@@ -582,7 +568,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "43/43 [==============================] - 0s 2ms/step\n"
+      "43/43 [==============================] - 0s 3ms/step\n"
      ]
     }
    ],
@@ -633,7 +619,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -792,7 +778,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7588204318062138"
+       "0.7498683517640864"
       ]
      },
      "execution_count": 32,

From 9646a7e5b27065c744c32802f3a3651b79d4f0c4 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:15:47 +1000
Subject: [PATCH 52/82] reformat link_mappers.py with black

---
 stellargraph/mapper/link_mappers.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
index 3250b941f..a19d64041 100755
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -210,7 +210,7 @@ class OnDemandLinkSequence(Sequence):
     """
 
     def __init__(self, generator, walker):
-        
+
         self.lock = threading.Lock()
 
         if isinstance(generator, GraphSAGELinkGenerator) or isinstance(
@@ -225,9 +225,9 @@ def __init__(self, generator, walker):
             )
 
         if isinstance(self.generator, GraphSAGELinkGenerator):
-            
+
             self.head_node_types = self.generator.schema.node_types * 2
-            
+
             # YT: we need to have self._sampling_schema for GraphSAGE.build() method to work
             self._sampling_schema = generator.schema.sampling_layout(
                 self.head_node_types, generator.num_samples
@@ -291,7 +291,7 @@ def __getitem__(self, batch_num):
         self.lock.release()
 
         if isinstance(self.generator, GraphSAGELinkGenerator):
-            
+
             if self.head_node_types is None:
 
                 # Get head node types from all src, dst nodes extracted from all links,

From cc67c8842f2a239732362cb67a7c0aa1bfaa07d1 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Wed, 25 Sep 2019 18:16:05 +1000
Subject: [PATCH 53/82] reformat the node_mappers.py file with black

---
 stellargraph/mapper/node_mappers.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py
index 9beeda0ab..6352abcdd 100755
--- a/stellargraph/mapper/node_mappers.py
+++ b/stellargraph/mapper/node_mappers.py
@@ -122,7 +122,6 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
                 )
             )
 
-        
         self.ids = list(ids)
         self.data_size = len(self.ids)
         self.shuffle = shuffle
@@ -131,7 +130,7 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
         self.on_epoch_end()
 
         if (
-            isinstance(self.generator, GraphSAGENodeGenerator) 
+            isinstance(self.generator, GraphSAGENodeGenerator)
             or isinstance(self.generator, DirectedGraphSAGENodeGenerator)
             or isinstance(self.generator, HinSAGENodeGenerator)
         ):
@@ -156,7 +155,6 @@ def __init__(self, generator, ids, targets=None, shuffle=True):
             self._sampling_schema = generator.schema.sampling_layout(
                 self.head_node_types, num_samples
             )
-            
 
     def __len__(self):
         """Denotes the number of batches per epoch"""

From 9270696222b62099b6e97fa4afc01f64567e86d4 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 15:42:07 +1000
Subject: [PATCH 54/82] Add the unit test for fun get_index_for_nodes

---
 tests/core/test_stellargraph.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/tests/core/test_stellargraph.py b/tests/core/test_stellargraph.py
index e0cfdb2ad..596e2df42 100644
--- a/tests/core/test_stellargraph.py
+++ b/tests/core/test_stellargraph.py
@@ -219,6 +219,24 @@ def test_digraph_schema():
     assert schema.get_edge_type((4, 0, 0)) == ("user", "rating", "movie")
     with pytest.raises(IndexError):
         schema.get_edge_type((0, 4, 0))
+        
+        
+def test_get_index_for_nodes():
+    sg = example_stellar_graph_1(feature_name="feature", feature_size=8)
+    aa = sg.get_index_for_nodes([1, 2, 3, 4])
+    assert aa == [0, 1, 2, 3]
+    
+    sg = example_hin_1(feature_name="feature")
+    aa = sg.get_index_for_nodes([0, 1, 2, 3])
+    assert aa == [0, 1, 2, 3]
+    aa = sg.get_index_for_nodes([0, 1, 2, 3], "A")
+    assert aa == [0, 1, 2, 3]
+    aa = sg.get_index_for_nodes([4, 5, 6])
+    assert aa == [0, 1, 2]
+    aa = sg.get_index_for_nodes([4, 5, 6], "B")
+    assert aa == [0, 1, 2]
+    with pytest.raises(ValueError):
+        aa = sg.get_index_for_nodes([1, 2, 5])
 
 
 def test_feature_conversion_from_nodes():

From 3099658c73ba6b714ba3ad7772d5843471c0f876 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 15:42:45 +1000
Subject: [PATCH 55/82] update test_node_mappers.py

---
 tests/mapper/test_node_mappers.py | 72 +------------------------------
 1 file changed, 2 insertions(+), 70 deletions(-)

diff --git a/tests/mapper/test_node_mappers.py b/tests/mapper/test_node_mappers.py
index 9002bf32b..f7b7ef2b2 100755
--- a/tests/mapper/test_node_mappers.py
+++ b/tests/mapper/test_node_mappers.py
@@ -751,88 +751,20 @@ def test_attri2vec_nodemapper_1():
         Attri2VecNodeGenerator(G1, batch_size=2).flow(["A", "B"])
 
 
-def test_attri2vec_nodemapper_shuffle():
+def test_attri2vec_nodemapper_2():
     n_feat = 1
     n_batch = 2
 
     G = example_graph_2(feature_size=n_feat)
     nodes = list(G.nodes())
 
-    # With shuffle
-    random.seed(15)
-    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
-        nodes, nodes, shuffle=True
-    )
-
-    expected_node_batches = [[5, 4], [3, 1], [2]]
-    assert len(mapper) == 3
-    for ii in range(len(mapper)):
-        nf, nl = mapper[ii]
-        assert all(np.ravel(nf) == expected_node_batches[ii])
-        assert all(np.array(nl) == expected_node_batches[ii])
-
-    # This should re-shuffle the IDs
-    mapper.on_epoch_end()
-    expected_node_batches = [[4, 3], [1, 5], [2]]
-    assert len(mapper) == 3
-    for ii in range(len(mapper)):
-        nf, nl = mapper[ii]
-        assert all(np.ravel(nf) == expected_node_batches[ii])
-        assert all(np.array(nl) == expected_node_batches[ii])
-
     # With no shuffle
-    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
-        nodes, nodes, shuffle=False
-    )
+    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(nodes)
     expected_node_batches = [[1, 2], [3, 4], [5]]
     assert len(mapper) == 3
     for ii in range(len(mapper)):
         nf, nl = mapper[ii]
         assert all(np.ravel(nf) == expected_node_batches[ii])
-        assert all(np.array(nl) == expected_node_batches[ii])
-
-
-def test_attri2vec_nodemapper_with_labels():
-    n_feat = 4
-    n_batch = 2
-
-    # test graph
-    G2 = example_graph_2(n_feat)
-    nodes = list(G2)
-    labels = [n * 2 for n in nodes]
-
-    gen = Attri2VecNodeGenerator(G2, batch_size=n_batch).flow(nodes, labels)
-    assert len(gen) == 3
-
-    for ii in range(3):
-        nf, nl = gen[ii]
-
-        # Check sizes - note batch sizes are (2,2,1) for each iteration
-        assert nf.shape[1:] == (n_feat,)
-
-        # Check labels
-        assert all(int(a) == int(2 * b) for a, b in zip(nl, nf[:, 0]))
-
-    # Check beyond the graph lengh
-    with pytest.raises(IndexError):
-        nf, nl = gen[len(gen)]
-
-
-def test_attri2vec_nodemapper_incorrect_targets():
-    """
-    Tests checks on target shape
-    """
-    n_feat = 4
-    n_batch = 2
-
-    # test graph
-    G = example_graph_1(feature_size=n_feat)
-
-    with pytest.raises(TypeError):
-        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), 1)
-
-    with pytest.raises(ValueError):
-        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), targets=[])
 
 
 def create_graph_features():

From 7f185da4a49024d0da0e66afeeae4f84d99ef4bc Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 15:43:40 +1000
Subject: [PATCH 56/82] update test_link_mappers.py

---
 tests/mapper/test_link_mappers.py | 40 ++++++++++++++-----------------
 1 file changed, 18 insertions(+), 22 deletions(-)

diff --git a/tests/mapper/test_link_mappers.py b/tests/mapper/test_link_mappers.py
index 8442cd088..f04b94ace 100755
--- a/tests/mapper/test_link_mappers.py
+++ b/tests/mapper/test_link_mappers.py
@@ -747,30 +747,26 @@ def test_Attri2VecLinkGenerator_1(self):
         with pytest.raises(IndexError):
             nf, nl = mapper[2]
 
-    def test_Attri2VecLinkGenerator_shuffle(self):
-        def test_edge_consistency(shuffle):
-            G = example_Graph_2(1)
-            edges = list(G.edges())
-            nodes = list(G.nodes())
-            edge_labels = list(range(len(edges)))
-
-            mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
-                edges, edge_labels, shuffle=shuffle
-            )
-
-            assert len(mapper) == 2
+    def test_edge_consistency(self):
+        G = example_Graph_2(1)
+        edges = list(G.edges())
+        nodes = list(G.nodes())
+        edge_labels = list(range(len(edges)))
+
+        mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
+            edges, edge_labels
+        )
 
-            for batch in range(len(mapper)):
-                nf, nl = mapper[batch]
-                e1 = edges[nl[0]]
-                e2 = edges[nl[1]]
-                assert nf[0][0, 0] == e1[0]
-                assert nf[1][0] == nodes.index(e1[1])
-                assert nf[0][1, 0] == e2[0]
-                assert nf[1][1] == nodes.index(e2[1])
+        assert len(mapper) == 2
 
-        test_edge_consistency(True)
-        test_edge_consistency(False)
+        for batch in range(len(mapper)):
+            nf, nl = mapper[batch]
+            e1 = edges[nl[0]]
+            e2 = edges[nl[1]]
+            assert nf[0][0, 0] == e1[0]
+            assert nf[1][0] == nodes.index(e1[1])
+            assert nf[0][1, 0] == e2[0]
+            assert nf[1][1] == nodes.index(e2[1])
 
     def test_Attri2VecLinkGenerator_not_Stellargraph(self):
         G = nx.Graph()

From 5e462f0bc921402903f480c97061aea94a48bec6 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 15:44:03 +1000
Subject: [PATCH 57/82] update test_attri2vec.py

---
 tests/layer/test_attri2vec.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tests/layer/test_attri2vec.py b/tests/layer/test_attri2vec.py
index 0701128a6..4bc8b9d79 100644
--- a/tests/layer/test_attri2vec.py
+++ b/tests/layer/test_attri2vec.py
@@ -52,6 +52,10 @@ def test_attri2vec_constructor():
     assert attri2vec.n_layers == 1
     assert attri2vec.bias == False
 
+    # Check incorrect activation flag
+    with pytest.raises(ValueError):
+        Attri2Vec(layer_sizes=[4], input_dim=2, node_num=4, activation="unknown")
+        
     # Check incorrect normalization flag
     with pytest.raises(ValueError):
         Attri2Vec(layer_sizes=[4], input_dim=2, node_num=4, normalize=lambda x: x)

From c4cf43eb3d93cf602bb259799040d966230c15b8 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:21:30 +1000
Subject: [PATCH 58/82] Revert "update test_link_mappers.py"

This reverts commit 7f185da4a49024d0da0e66afeeae4f84d99ef4bc.
---
 tests/core/test_stellargraph.py   | 18 --------
 tests/mapper/test_link_mappers.py | 40 +++++++++--------
 tests/mapper/test_node_mappers.py | 72 ++++++++++++++++++++++++++++++-
 3 files changed, 92 insertions(+), 38 deletions(-)

diff --git a/tests/core/test_stellargraph.py b/tests/core/test_stellargraph.py
index 596e2df42..e0cfdb2ad 100644
--- a/tests/core/test_stellargraph.py
+++ b/tests/core/test_stellargraph.py
@@ -219,24 +219,6 @@ def test_digraph_schema():
     assert schema.get_edge_type((4, 0, 0)) == ("user", "rating", "movie")
     with pytest.raises(IndexError):
         schema.get_edge_type((0, 4, 0))
-        
-        
-def test_get_index_for_nodes():
-    sg = example_stellar_graph_1(feature_name="feature", feature_size=8)
-    aa = sg.get_index_for_nodes([1, 2, 3, 4])
-    assert aa == [0, 1, 2, 3]
-    
-    sg = example_hin_1(feature_name="feature")
-    aa = sg.get_index_for_nodes([0, 1, 2, 3])
-    assert aa == [0, 1, 2, 3]
-    aa = sg.get_index_for_nodes([0, 1, 2, 3], "A")
-    assert aa == [0, 1, 2, 3]
-    aa = sg.get_index_for_nodes([4, 5, 6])
-    assert aa == [0, 1, 2]
-    aa = sg.get_index_for_nodes([4, 5, 6], "B")
-    assert aa == [0, 1, 2]
-    with pytest.raises(ValueError):
-        aa = sg.get_index_for_nodes([1, 2, 5])
 
 
 def test_feature_conversion_from_nodes():
diff --git a/tests/mapper/test_link_mappers.py b/tests/mapper/test_link_mappers.py
index f04b94ace..8442cd088 100755
--- a/tests/mapper/test_link_mappers.py
+++ b/tests/mapper/test_link_mappers.py
@@ -747,26 +747,30 @@ def test_Attri2VecLinkGenerator_1(self):
         with pytest.raises(IndexError):
             nf, nl = mapper[2]
 
-    def test_edge_consistency(self):
-        G = example_Graph_2(1)
-        edges = list(G.edges())
-        nodes = list(G.nodes())
-        edge_labels = list(range(len(edges)))
-
-        mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
-            edges, edge_labels
-        )
+    def test_Attri2VecLinkGenerator_shuffle(self):
+        def test_edge_consistency(shuffle):
+            G = example_Graph_2(1)
+            edges = list(G.edges())
+            nodes = list(G.nodes())
+            edge_labels = list(range(len(edges)))
 
-        assert len(mapper) == 2
+            mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
+                edges, edge_labels, shuffle=shuffle
+            )
 
-        for batch in range(len(mapper)):
-            nf, nl = mapper[batch]
-            e1 = edges[nl[0]]
-            e2 = edges[nl[1]]
-            assert nf[0][0, 0] == e1[0]
-            assert nf[1][0] == nodes.index(e1[1])
-            assert nf[0][1, 0] == e2[0]
-            assert nf[1][1] == nodes.index(e2[1])
+            assert len(mapper) == 2
+
+            for batch in range(len(mapper)):
+                nf, nl = mapper[batch]
+                e1 = edges[nl[0]]
+                e2 = edges[nl[1]]
+                assert nf[0][0, 0] == e1[0]
+                assert nf[1][0] == nodes.index(e1[1])
+                assert nf[0][1, 0] == e2[0]
+                assert nf[1][1] == nodes.index(e2[1])
+
+        test_edge_consistency(True)
+        test_edge_consistency(False)
 
     def test_Attri2VecLinkGenerator_not_Stellargraph(self):
         G = nx.Graph()
diff --git a/tests/mapper/test_node_mappers.py b/tests/mapper/test_node_mappers.py
index f7b7ef2b2..9002bf32b 100755
--- a/tests/mapper/test_node_mappers.py
+++ b/tests/mapper/test_node_mappers.py
@@ -751,20 +751,88 @@ def test_attri2vec_nodemapper_1():
         Attri2VecNodeGenerator(G1, batch_size=2).flow(["A", "B"])
 
 
-def test_attri2vec_nodemapper_2():
+def test_attri2vec_nodemapper_shuffle():
     n_feat = 1
     n_batch = 2
 
     G = example_graph_2(feature_size=n_feat)
     nodes = list(G.nodes())
 
+    # With shuffle
+    random.seed(15)
+    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
+        nodes, nodes, shuffle=True
+    )
+
+    expected_node_batches = [[5, 4], [3, 1], [2]]
+    assert len(mapper) == 3
+    for ii in range(len(mapper)):
+        nf, nl = mapper[ii]
+        assert all(np.ravel(nf) == expected_node_batches[ii])
+        assert all(np.array(nl) == expected_node_batches[ii])
+
+    # This should re-shuffle the IDs
+    mapper.on_epoch_end()
+    expected_node_batches = [[4, 3], [1, 5], [2]]
+    assert len(mapper) == 3
+    for ii in range(len(mapper)):
+        nf, nl = mapper[ii]
+        assert all(np.ravel(nf) == expected_node_batches[ii])
+        assert all(np.array(nl) == expected_node_batches[ii])
+
     # With no shuffle
-    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(nodes)
+    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
+        nodes, nodes, shuffle=False
+    )
     expected_node_batches = [[1, 2], [3, 4], [5]]
     assert len(mapper) == 3
     for ii in range(len(mapper)):
         nf, nl = mapper[ii]
         assert all(np.ravel(nf) == expected_node_batches[ii])
+        assert all(np.array(nl) == expected_node_batches[ii])
+
+
+def test_attri2vec_nodemapper_with_labels():
+    n_feat = 4
+    n_batch = 2
+
+    # test graph
+    G2 = example_graph_2(n_feat)
+    nodes = list(G2)
+    labels = [n * 2 for n in nodes]
+
+    gen = Attri2VecNodeGenerator(G2, batch_size=n_batch).flow(nodes, labels)
+    assert len(gen) == 3
+
+    for ii in range(3):
+        nf, nl = gen[ii]
+
+        # Check sizes - note batch sizes are (2,2,1) for each iteration
+        assert nf.shape[1:] == (n_feat,)
+
+        # Check labels
+        assert all(int(a) == int(2 * b) for a, b in zip(nl, nf[:, 0]))
+
+    # Check beyond the graph lengh
+    with pytest.raises(IndexError):
+        nf, nl = gen[len(gen)]
+
+
+def test_attri2vec_nodemapper_incorrect_targets():
+    """
+    Tests checks on target shape
+    """
+    n_feat = 4
+    n_batch = 2
+
+    # test graph
+    G = example_graph_1(feature_size=n_feat)
+
+    with pytest.raises(TypeError):
+        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), 1)
+
+    with pytest.raises(ValueError):
+        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), targets=[])
 
 
 def create_graph_features():

From e89940f6619e5df35a890d01e10b14283006a36b Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:22:08 +1000
Subject: [PATCH 59/82] test_attri2vec.py update

---
 tests/layer/test_attri2vec.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/layer/test_attri2vec.py b/tests/layer/test_attri2vec.py
index 4bc8b9d79..70b7f9f1b 100644
--- a/tests/layer/test_attri2vec.py
+++ b/tests/layer/test_attri2vec.py
@@ -55,7 +55,7 @@ def test_attri2vec_constructor():
     # Check incorrect activation flag
     with pytest.raises(ValueError):
         Attri2Vec(layer_sizes=[4], input_dim=2, node_num=4, activation="unknown")
-        
+
     # Check incorrect normalization flag
     with pytest.raises(ValueError):
         Attri2Vec(layer_sizes=[4], input_dim=2, node_num=4, normalize=lambda x: x)

From 01c06eb26e6144e1c829301d600b3a748848c1a7 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:23:04 +1000
Subject: [PATCH 60/82] update attri2vec link prediction demo on DBLP

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb b/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
index 17e8cfa88..346775154 100644
--- a/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
+++ b/demos/link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb
@@ -470,10 +470,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 17:46:06.513718 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W1001 16:10:36.693701 140735704433600 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 17:46:06.534775 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W1001 16:10:36.716068 140735704433600 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -550,7 +550,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0925 17:46:07.069445 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W1001 16:10:37.142501 140735704433600 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -560,17 +560,17 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/6\n",
-      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.7018 - binary_accuracy: 0.6224\n",
+      "4711/4711 [==============================] - 82s 17ms/step - loss: 0.7637 - binary_accuracy: 0.5855\n",
       "Epoch 2/6\n",
-      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.6562 - binary_accuracy: 0.6042\n",
+      "4711/4711 [==============================] - 77s 16ms/step - loss: 0.6452 - binary_accuracy: 0.6117\n",
       "Epoch 3/6\n",
-      "4711/4711 [==============================] - 75s 16ms/step - loss: 0.5475 - binary_accuracy: 0.6931\n",
+      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.5849 - binary_accuracy: 0.6489\n",
       "Epoch 4/6\n",
-      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.4017 - binary_accuracy: 0.8048\n",
+      "4711/4711 [==============================] - 78s 17ms/step - loss: 0.4923 - binary_accuracy: 0.7270\n",
       "Epoch 5/6\n",
-      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.2637 - binary_accuracy: 0.8900\n",
+      "4711/4711 [==============================] - 77s 16ms/step - loss: 0.3702 - binary_accuracy: 0.8201\n",
       "Epoch 6/6\n",
-      "4711/4711 [==============================] - 76s 16ms/step - loss: 0.1570 - binary_accuracy: 0.9473\n"
+      "4711/4711 [==============================] - 80s 17ms/step - loss: 0.2246 - binary_accuracy: 0.9119\n"
      ]
     }
    ],
@@ -770,7 +770,7 @@
     {
      "data": {
       "text/plain": [
-       "0.8085538458796013"
+       "0.7927089536523324"
       ]
      },
      "execution_count": 33,
@@ -881,7 +881,7 @@
     {
      "data": {
       "text/plain": [
-       "0.6651603215420048"
+       "0.6628765045431108"
       ]
      },
      "execution_count": 38,

From d592cbe6b8f62457bfc6e4b9fd2cfc91717b1dd6 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:23:36 +1000
Subject: [PATCH 61/82] delete attri2vec DBLP embedding learning demo

---
 .../stellargraph-attri2vec-DBLP.ipynb         | 601 ------------------
 1 file changed, 601 deletions(-)
 delete mode 100644 demos/embeddings/stellargraph-attri2vec-DBLP.ipynb

diff --git a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb b/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
deleted file mode 100644
index b49475e58..000000000
--- a/demos/embeddings/stellargraph-attri2vec-DBLP.ipynb
+++ /dev/null
@@ -1,601 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Node Representation Learning with attri2vec on DBLP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
-    "\n",
-    "\n",
-    "## attri2vec\n",
-    "\n",
-    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
-    "\n",
-    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
-    "\n",
-    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
-    "\n",
-    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "import stellargraph as sg\n",
-    "from stellargraph.data import UnsupervisedSampler\n",
-    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
-    "from stellargraph.layer import Attri2Vec, link_classification\n",
-    "\n",
-    "from tensorflow import keras\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
-    "from sklearn.metrics import accuracy_score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading DBLP network data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This demo uses a DBLP citation network, a subgraph extracted from [DBLP-Citation-network V3](https://aminer.org/citation). To form this subgraph, papers from four subjects are extracted according to their venue information: *Database, Data Mining, Artificial Intelligence and Computer Vision*, and papers with no citations are removed. The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word. By ignoring the citation direction, we take the DBLP subgraph as an undirected network.\n",
-    "\n",
-    "The dataset used in this demo can be downloaded from https://www.kaggle.com/daozhang/dblp-subgraph.\n",
-    "The following is the description of the dataset:\n",
-    "\n",
-    "> The content.txt file contains descriptions of the papers in the following format:\n",
-    "\n",
-    " \t\t<paper_id> <word_attributes> <class_label> <publication_year>\n",
-    "        \n",
-    "> The first entry in each line contains the unique integer ID (ranging from 0 to 18,447) of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last two entries in the line are the class label and the publication year of the paper.\n",
-    "> The edgeList.txt file contains the citation relations. Each line describes a link in the following format:\n",
-    "\t\t\n",
-    "        <ID of paper1> <ID of paper2>\n",
-    "        \n",
-    "> Each line contains two paper IDs, with paper2 citing paper1 or paper1 citing paper2.\n",
-    "\n",
-    "\n",
-    "Download and unzip the dblp-subgraph.zip file to a location on your computer and set the `data_dir` variable to\n",
-    "point to the location of the dataset (the \"DBLP\" directory containing \"content.txt\" and \"edgeList.txt\")."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dir = \"~/data/DBLP\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load the graph from the edgelist."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edgelist = pd.read_csv(os.path.join(data_dir, \"edgeList.txt\"), sep='\\t', header=None, names=[\"source\", \"target\"])\n",
-    "edgelist[\"label\"] = \"cites\"  # set the edge type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load paper content features, subjects and publishing years."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_names = [\"w_{}\".format(ii) for ii in range(2476)]\n",
-    "node_column_names =  feature_names + [\"subject\", \"year\"]\n",
-    "node_data = pd.read_csv(os.path.join(data_dir, \"content.txt\"), sep='\\t', header=None, names=node_column_names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Construct the graph from edge list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify node types."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nx.set_node_attributes(G_nx, \"paper\", \"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_features = node_data[feature_names]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the Stellargraph with node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = sg.StellarGraph(G_nx, node_features=node_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 18448, Edges: 45611\n",
-      "\n",
-      " Node types:\n",
-      "  paper: [18448]\n",
-      "    Edge types: paper-cites->paper\n",
-      "\n",
-      " Edge types:\n",
-      "    paper-cites->paper: [45611]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(G.info())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train attri2vec on the DBLP Subgraph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes = list(G.nodes())\n",
-    "number_of_walks = 4\n",
-    "length = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set the batch size and the number of epochs. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 50\n",
-    "epochs = 8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Attri2VecLinkGenerator with an estimated 14758 batches generated on the fly per epoch.\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
-    "\n",
-    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "layer_sizes = [128]\n",
-    "attri2vec = Attri2Vec(\n",
-    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 16:19:56.064584 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 16:19:56.085483 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
-    "x_inp, x_out = attri2vec.build()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
-     ]
-    }
-   ],
-   "source": [
-    "prediction = link_classification(\n",
-    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
-    ")(x_out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
-    "    loss=keras.losses.binary_crossentropy,\n",
-    "    metrics=[keras.metrics.binary_accuracy],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Train the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/8\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "W0925 16:19:56.485485 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14759/14759 [==============================] - 371s 25ms/step - loss: 0.7013 - binary_accuracy: 0.5400\n",
-      "Epoch 2/8\n",
-      "14759/14759 [==============================] - 373s 25ms/step - loss: 0.6139 - binary_accuracy: 0.6296\n",
-      "Epoch 3/8\n",
-      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.5194 - binary_accuracy: 0.7376\n",
-      "Epoch 4/8\n",
-      "14759/14759 [==============================] - 376s 25ms/step - loss: 0.4491 - binary_accuracy: 0.7930\n",
-      "Epoch 5/8\n",
-      "14759/14759 [==============================] - 378s 26ms/step - loss: 0.4003 - binary_accuracy: 0.8237\n",
-      "Epoch 6/8\n",
-      "14759/14759 [==============================] - 382s 26ms/step - loss: 0.3669 - binary_accuracy: 0.8434\n",
-      "Epoch 7/8\n",
-      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.3407 - binary_accuracy: 0.8566\n",
-      "Epoch 8/8\n",
-      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.3204 - binary_accuracy: 0.8673\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit_generator(\n",
-    "    train_gen,\n",
-    "    epochs=epochs,\n",
-    "    verbose=1,\n",
-    "    use_multiprocessing=False,\n",
-    "    workers=1,\n",
-    "    shuffle=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Visualise Node Embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_inp_src = x_inp[0]\n",
-    "x_out_src = x_out[0]\n",
-    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the node embeddings by applying the learned mapping function to node content features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "369/369 [==============================] - 1s 2ms/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "node_ids = node_data.index\n",
-    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
-    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get node subjects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_targets = [ node_data[\"subject\"][node_id] for node_id in node_ids ]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Transform the embeddings to 2d space for visualisation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform = TSNE # PCA\n",
-    "\n",
-    "trans = transform(n_components=2)\n",
-    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# draw the embedding points, coloring them by the target label (paper subject)\n",
-    "alpha = 0.7\n",
-    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
-    "node_colours = [ label_map[target] for target in node_targets ]\n",
-    "\n",
-    "plt.figure(figsize=(7,7))\n",
-    "plt.axes().set(aspect=\"equal\")\n",
-    "plt.scatter(node_embeddings_2d[:,0], \n",
-    "            node_embeddings_2d[:,1], \n",
-    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
-    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From e375e81895f36de887e61128530eb81d40ab7822 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:23:57 +1000
Subject: [PATCH 62/82] update attri2vec citeseer embedding learning demo

---
 .../stellargraph-attri2vec-citeseer.ipynb     | 40 ++++++++++++++-----
 1 file changed, 30 insertions(+), 10 deletions(-)

diff --git a/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb b/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
index ada0dbbcc..146b2d9b5 100644
--- a/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
+++ b/demos/embeddings/stellargraph-attri2vec-citeseer.ipynb
@@ -416,10 +416,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 17:39:33.838998 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W1001 15:58:33.015388 140735704433600 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 17:39:33.886423 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W1001 15:58:33.042335 140735704433600 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -492,11 +492,18 @@
     "scrolled": true
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/4\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0925 17:39:34.398716 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W1001 15:58:33.300551 140735704433600 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -505,14 +512,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/4\n",
-      "1688/1688 [==============================] - 20s 12ms/step - loss: 0.6833 - binary_accuracy: 0.5538\n",
+      "1688/1688 [==============================] - 12s 7ms/step - loss: 0.6825 - binary_accuracy: 0.5534\n",
       "Epoch 2/4\n",
-      "1688/1688 [==============================] - 18s 11ms/step - loss: 0.5183 - binary_accuracy: 0.7489\n",
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.5160 - binary_accuracy: 0.7534\n",
       "Epoch 3/4\n",
-      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.3391 - binary_accuracy: 0.8740\n",
+      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3368 - binary_accuracy: 0.8759\n",
       "Epoch 4/4\n",
-      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.2485 - binary_accuracy: 0.9145\n"
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2475 - binary_accuracy: 0.9150\n"
      ]
     }
    ],
@@ -568,7 +574,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "43/43 [==============================] - 0s 3ms/step\n"
+      "43/43 [==============================] - 0s 4ms/step\n"
      ]
     }
    ],
@@ -619,7 +625,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -644,6 +650,20 @@
     "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downstream Task"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The node representations learned by attri2vec can be used as features for downstream tasks, such as node classification, community detection, and link prediction. As attri2vec inductively learns a mapping function from node content features to the target embeddings, it has the ability to infer the representations for out-of-sample nodes, which can be used to predict the labels or links of out-of-sample nodes."
+   ]
   }
  ],
  "metadata": {

From 99160974072acbaa3a65d10f8d985ace9807c46c Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:25:14 +1000
Subject: [PATCH 63/82] update demos/node-classification/attri2vec/README.md
 file

---
 demos/node-classification/attri2vec/README.md | 39 +++++--------------
 1 file changed, 10 insertions(+), 29 deletions(-)

diff --git a/demos/node-classification/attri2vec/README.md b/demos/node-classification/attri2vec/README.md
index 89355cc18..b28d99268 100644
--- a/demos/node-classification/attri2vec/README.md
+++ b/demos/node-classification/attri2vec/README.md
@@ -1,39 +1,33 @@
 ## Node classification using attri2vec [1]
 
-This folder contains two [Jupyter](http://jupyter.org/) python notebooks demonstrating the combined use of
-`stellargraph` (this library), `Scikit-learn` [2] libraries for node classification in two homogeneous graphs
+This folder contains a [Jupyter](http://jupyter.org/) python notebook demonstrating the combined use of
+`stellargraph` (this library) and `Scikit-learn` [2] libraries for node classification in a homogeneous graph
 attached with node attributes.
 
-The first example demonstrates node representation learning and node classification using the citeseer
+The example demonstrates node representation learning and node classification using the citeseer
 paper citation network. This demo is included in the Jupyter notebook
 `attri2vec-citeseer-node-classification-example.ipynb`.
 
-The second example demonstrates node representation learning and node classification using the DBLP
-paper citation network. This demo is included in the Jupyter notebook
-`attri2vec-DBLP-node-classification-example.ipynb`.
-
-The two Jupyter notebooks include all the information for downloading the corresponding datasets, training the GraphSAGE
-models and using them to classify nodes with unknown (to the training algorithm) labels.
+The notebook includes all the information for downloading the corresponding dataset, training the attri2vec
+model and using it to classify nodes with unknown (to the training algorithm) labels.
 
-To run the notebooks, install Jupyter to the same Python 3.6 environment as StellarGraph, following the instructions on
+To run the notebook, install Jupyter to the same Python 3.6 environment as StellarGraph, following the instructions on
 the Jupyter project website: http://jupyter.org/install.html
 
-After starting the Jupyter server on your computer, load either of the two notebooks and follow the instructions inside.
+After starting the Jupyter server on your computer, load the notebook and follow the instructions inside.
 
 ## Requirements
 
-All examples use Python 3.6 and the StellarGraph library. To install the StellarGraph library
+The example uses Python 3.6 and the StellarGraph library. To install the StellarGraph library
 follow the instructions at: https://github.com/stellargraph/stellargraph
 
 Additional requirements are Pandas, Numpy and Scikit-Learn which are installed as dependencies
-of the StellarGraph library. In addition Juptyer is required to run the notebook version of
+of the StellarGraph library. In addition, Juptyer is required to run the notebook version of
 the example.
 
 ## Dataset
 
-The examples in this directory uses the citeseer and DBLP dataset.
-
-The citeseer dataset can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz).
+The example in this directory uses the citeseer dataset, which can be downloaded from [here](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz).
 
 The following is the description of the dataset:
 > The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes.
@@ -44,19 +38,6 @@ The following is the description of the dataset:
 Download and unzip the [citeseer.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/citesser.tgz) file to a location on your
 computer and pass this location as a command line argument to this script.
 
-The DBLP dataset can be downloaded from [here](https://www.kaggle.com/daozhang/dblp-subgraph).
-
-The following is the description of the dataset:
-> The DBLP citation network is a subgraph extracted from DBLP-Citation-network V3 (https://aminer.org/citation).
-> To form this subgraph, papers from four subjects are extracted according to their venue information:
-> Database, Data Mining, Artificial Intelligence and Computer Vision, and papers with no citations are removed.
-> The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct
-> 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word.
-> By ignoring the citation direction, we take the DBLP subgraph as an undirected network.
-
-Download and unzip the [DBLP.zip](https://www.kaggle.com/daozhang/dblp-subgraph) file to a location on your computer
-and pass this location as a command line argument to this script.
-
 ## References
 
 **1.** Attributed Network Embedding via Subspace Discovery. D. Zhang, J, Yin, X. Zhu and C. Zhang, arXiv:1901.04095,

From f74eadb64a1062c70f3b52b983683acec9f03f85 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:25:38 +1000
Subject: [PATCH 64/82] update demos/node-classification/README.md file

---
 demos/node-classification/README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/demos/node-classification/README.md b/demos/node-classification/README.md
index f3d42bcc1..343d2967a 100644
--- a/demos/node-classification/README.md
+++ b/demos/node-classification/README.md
@@ -9,12 +9,12 @@ Each folder contains one or more examples of using the StellarGraph implementati
 state-of-the-art algorithms, attri2vec[4], GraphSAGE [3], HinSAGE, GCN [6], GAT [7], SGC [9], Node2Vec [1], and Metapath2Vec [2].
 GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [6]. Node2Vec and
 Metapath2Vec are methods based on graph random walks and representation learning using the
-Word2Vec [5] algorithm. attri2vec[4] is also based on graph random walks and learns node
+Word2Vec [5] algorithm. attri2vec[4] is also based on graph random walks, and learns node
 representations by performing a mapping on node attributes.
 
 The examples folder structure is shown below.
 
-* [`/attri2vec`] (https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/graphsage)
+* [`/attri2vec`](https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/graphsage)
 
     Examples of supervised node classification for two homogeneous networks with attributes, using the attri2vec algorithm [4].
 
@@ -24,15 +24,15 @@ The examples folder structure is shown below.
 
 * [`/gcn`](https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/gcn)
 
-    Example of semi-supervised node classification for a homogeneous network, using the GCN algorithm [5].
+    Example of semi-supervised node classification for a homogeneous network, using the GCN algorithm [6].
 
 * [`/sgc`](https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/sgc)
 
-    Example of semi-supervised node classification for a homogeneous network, using the SGC algorithm [8].
+    Example of semi-supervised node classification for a homogeneous network, using the SGC algorithm [9].
 
 * [`/gat`](https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/gat)
 
-    Example of supervised node classification for a homogeneous network with attributed nodes, using the GAT algorithm [6].
+    Example of supervised node classification for a homogeneous network with attributed nodes, using the GAT algorithm [7].
 
 * [`/node2vec`](https://github.com/stellargraph/stellargraph/tree/master/demos/node-classification/node2vec)
 

From 9a217cc896a10614c63a7787ebb2259710d80e7b Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:26:04 +1000
Subject: [PATCH 65/82] update demos/link-prediction/attri2vec/README.md file

---
 demos/link-prediction/attri2vec/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/link-prediction/attri2vec/README.md b/demos/link-prediction/attri2vec/README.md
index 1fd1b57ba..8ce7a6251 100644
--- a/demos/link-prediction/attri2vec/README.md
+++ b/demos/link-prediction/attri2vec/README.md
@@ -4,7 +4,7 @@ This is an example of using the attri2vec [1] model, with a link classifier on t
 to predict links for out-of-sample nodes in a homogeneous citation network.
 
 In this demo, we first train the attri2vec model on the in-sample subgraph and infer
-representations for out-of-sample nodes with the trained attri2vec model. The we use the
+representations for out-of-sample nodes with the trained attri2vec model. Then we use the
 obtained node representations to perform link prediction for out-of-sample nodes.
 
 The link prediction problem is treated as a supervised binary classification problem for

From 536ade18019bd6d3c1f565d4a17791d6c0fecd6f Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:26:29 +1000
Subject: [PATCH 66/82] update demos/link-prediction/README.md file

---
 demos/link-prediction/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/demos/link-prediction/README.md b/demos/link-prediction/README.md
index 77a98fb58..9b8467077 100644
--- a/demos/link-prediction/README.md
+++ b/demos/link-prediction/README.md
@@ -6,7 +6,7 @@ examples demonstrate using the `StellarGraph` library to build machine learning
 workflows on both homogeneous and heterogeneous networks.
 
 Each folder contains one or more examples of using the StellarGraph implementations of the
-state-of-the-art algorithms, attri2vec[4], GraphSAGE [3], HinSAGE, GCN [6], GAT [7], Node2Vec [1], and Metapath2Vec [2].
+state-of-the-art algorithms, attri2vec [4], GraphSAGE [3], HinSAGE, GCN [6], GAT [7], Node2Vec [1], and Metapath2Vec [2].
 GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [6]. Node2Vec and
 Metapath2Vec are methods based on graph random walks and representation learning using the
 Word2Vec [5] algorithm. attri2vec[4] is also based on graph random walks and learns node
@@ -29,7 +29,7 @@ The examples folder structure is shown below.
     Example of supervised link attribute prediction for a heterogeneous network with attributed nodes of different types,
     using the HinSAGE algorithm.
 
-* [`/attri2vec`] (https://github.com/stellargraph/stellargraph/tree/master/demos/link-prediction/attri2vec)
+* [`/attri2vec`](https://github.com/stellargraph/stellargraph/tree/master/demos/link-prediction/attri2vec)
 
     Example of link prediction for out-of-sample nodes for a homogeneous network with attributed nodes,
     using the attri2vec algorithm.

From 8ae2f9b6e8c573e535699468fe67d8a185feebb4 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:26:48 +1000
Subject: [PATCH 67/82] update demos/embeddings/README.md file

---
 demos/embeddings/README.md | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/demos/embeddings/README.md b/demos/embeddings/README.md
index 1a7ce176d..688ec38b5 100644
--- a/demos/embeddings/README.md
+++ b/demos/embeddings/README.md
@@ -1,17 +1,16 @@
 ## Representation Learning Examples
 
-This folder contains three [Jupyter](http://jupyter.org/) python notebooks demonstrating the use of unsupervised graph representation learning methods implemented in the `stellargraph` library for homogeneous and hetrogenous graphs with or without node features. The original works are referenced below.
+This folder contains four [Jupyter](http://jupyter.org/) python notebooks demonstrating the use of unsupervised graph representation learning methods implemented in the `stellargraph` library for homogeneous and heterogenous graphs with or without node features. The original works are referenced below.
 
 **Node2Vec** and **Metapath2Vec** notebooks demonstrate the combined use of `stellargraph` and `Gensim` [4] libraries for representation learning on homogeneous and heterogeneous graphs.
 **Unsupervised GraphSAGE** notebook demonstrates the use of `Stellargraph` library's GraphSAGE implementation for unsupervised learning of node embeddings for homogeneous graphs with node features.
 **attri2vec** notebook demonstrates the implementation of attri2vec with the `Stellargraph` library for unsupervised inductive learning of node embeddings for homogeneous graphs with node features, and the evaluation for its ability to infer the representations of out-of-sample nodes with the out-of-sample node link prediction task.   
 
 The notebooks demonstrate the following algorithms.
-- `stellargraph-node2vec.ipynb` The **Node2Vec** algorithm [1] for representation learning on homogeneous graphs
+- `stellargraph-node2vec.ipynb` The **Node2Vec** algorithm [1] for representation learning on homogeneous graphs.
 - `stellargraph-metapath2vec.ipynb` The **Metapath2Vec** algorithm [2] for representation learning on heterogeneous graphs.
 - `embeddings-unsupervised-graphsage-cora.ipynb` The **Unsupervised GraphSAGE** algorithm [5] for representation learning on homogeneous graphs with node features.
-- `stellargraph-attri2vec-citeseer.ipynb` The **attri2vec** algorithm [6] for representation learning on the homogeneous graph citeseer with node features.
-- `stellargraph-attri2vec-DBLP.ipynb` The **attri2vec** algorithm [6] for representation learning on the homogeneous graph DBLP with node features.
+- `stellargraph-attri2vec-citeseer.ipynb` The **attri2vec** algorithm [6] for representation learning on the homogeneous graph with node features.
 
 All examples demonstrate how to calculate embedding vectors for a graph's nodes in just a few lines of Python code.
 The learned node representations can be used in numerous downstream tasks such as node attribute inference, link

From 6cf8cbef329cee57f8356cd35e53c0209eeec323 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:27:01 +1000
Subject: [PATCH 68/82] update demos/README.md file

---
 demos/README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/demos/README.md b/demos/README.md
index 12c117cd5..a2311696e 100644
--- a/demos/README.md
+++ b/demos/README.md
@@ -10,7 +10,8 @@ Each folder contains one or more examples of using the StellarGraph implementati
 state-of-the-art algorithms, attri2vec [4], GraphSAGE [3], HinSAGE, GCN [6], GAT [7], Node2Vec [1], and Metapath2Vec [2].
 GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [6]. Node2Vec and
 Metapath2Vec are methods based on graph random walks and representation learning using the
-Word2Vec [5] algorithm.
+Word2Vec [5] algorithm. attri2vec[4] is also based on graph random walks, and learns node
+representations by performing a mapping on node attributes.
 
 The examples folder structure is shown below.
 

From 94cb345507c145909a7bf25b2d3dd42eee2c263a Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:27:15 +1000
Subject: [PATCH 69/82] update README.md file

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 18bf87413..2ac3cd6df 100644
--- a/README.md
+++ b/README.md
@@ -193,7 +193,7 @@ The StellarGraph library currently includes the following algorithms for graph m
   The current implementation supports mean aggregation of neighbour nodes,
   taking into account their types and the types of links between them.
 
-* Attri2vec [4]
+* attri2vec [4]
   - Supports node representation learning, node classification, and out-of-sample node link prediction for homogeneous graphs with node attributes.
 
 * The Graph ATtention Network (GAT) [5]

From 291628ff89132b29224f48b0a5ac9a7ef85becc3 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:27:41 +1000
Subject: [PATCH 70/82] update node_mappers.py

---
 stellargraph/mapper/node_mappers.py | 63 ++++++++++-------------------
 1 file changed, 21 insertions(+), 42 deletions(-)

diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py
index 6352abcdd..50e076ec2 100755
--- a/stellargraph/mapper/node_mappers.py
+++ b/stellargraph/mapper/node_mappers.py
@@ -329,7 +329,7 @@ def flow(self, node_ids, targets=None, shuffle=False):
         supplied node_ids to be used by the downstream task. They should
         be given in the same order as the list of node IDs.
         If they are not specified (for example, for use in prediction),
-        the targets will not be available to the downsteam task.
+        the targets will not be available to the downstream task.
 
         Note that the shuffle argument should be True for training and
         False for prediction.
@@ -493,7 +493,7 @@ def flow(self, node_ids, targets=None, shuffle=False):
         supplied node_ids to be used by the downstream task. They should
         be given in the same order as the list of node IDs.
         If they are not specified (for example, for use in prediction),
-        the targets will not be available to the downsteam task.
+        the targets will not be available to the downstream task.
 
         Note that the shuffle argument should be True for training and
         False for prediction.
@@ -539,7 +539,8 @@ def flow_from_dataframe(self, node_targets, shuffle=False):
 
 class Attri2VecNodeGenerator:
     """
-    A data generator for node prediction with attri2vec models
+    A node feature generator for node representation prediction with the 
+    attri2vec model.
 
     At minimum, supply the StellarGraph and the batch size.
 
@@ -547,14 +548,13 @@ class Attri2VecNodeGenerator:
     machine learning. Currently the model requires node features for all
     nodes in the graph.
 
-    Use the :meth:`flow` method supplying the nodes and (optionally) targets
-    to get an object that can be used as a Keras data generator.
+    Use the :meth:`flow` method supplying the nodes to get an object 
+    that can be used as a Keras data generator.
 
     Example::
 
         G_generator = Attri2VecNodeGenerator(G, 50)
-        train_data_gen = G_generator.flow(train_node_ids, train_node_labels)
-        test_data_gen = G_generator.flow(test_node_ids)
+        data_gen = G_generator.flow(node_ids)
 
     Args:
         G (StellarGraph): The machine-learning ready graph.
@@ -580,69 +580,48 @@ def sample_features(self, head_nodes):
 
         Args:
             head_nodes: An iterable of head nodes to perform sampling on.
-            sampling_schema: The sampling schema for the model, for compatibility with GraphSAGE and HinSAGE.
 
         Returns:
-            A list of feaure arrys, with each element being the feature of a
+            A list of feature arrays, with each element being the feature of a
             head node.
         """
 
         batch_feats = self.graph.get_feature_for_nodes(head_nodes)
         return batch_feats
 
-    def flow(self, node_ids, targets=None, shuffle=False):
+    def flow(self, node_ids):
         """
-        Creates a generator/sequence object for training or evaluation
-        with the supplied node ids and numeric targets.
+        Creates a generator/sequence object for evaluation
+        with the supplied node ids.
 
-        The node IDs are the nodes to train or inference on: the embeddings
+        The node IDs are the nodes to inference on: the embeddings
         calculated for these nodes are passed to the downstream task. These
-        are a subset of the nodes in the graph.
-
-        The targets are an array of numeric targets corresponding to the
-        supplied node_ids to be used by the downstream task. They should
-        be given in the same order as the list of node IDs.
-        If they are not specified (for example, for use in prediction),
-        the targets will not be available to the downsteam task.
-
-        Note that the shuffle argument should be True for training and
-        False for prediction.
+        are a subset/all of the nodes in the graph.
 
         Args:
             node_ids: an iterable of node IDs.
-            targets: a 2D array of numeric targets with shape
-                `(len(node_ids), target_size)`.
-            shuffle (bool): If True the node_ids will be shuffled at each
-                epoch, if False the node_ids will be processed in order.
 
         Returns:
             A NodeSequence object to use with the Attri2Vec model
-            in Keras methods ``fit_generator``, ``evaluate_generator``,
-            and ``predict_generator``.
+            in the Keras method ``predict_generator``.
 
         """
-        return NodeSequence(self, node_ids, targets, shuffle=shuffle)
+        return NodeSequence(self, node_ids, shuffle=False)
 
-    def flow_from_dataframe(self, node_targets, shuffle=False):
+    def flow_from_dataframe(self, node_ids):
         """
         Creates a generator/sequence object for training or evaluation
-        with the supplied node ids and numeric targets.
+        with the supplied node ids.
 
         Args:
-            node_targets: a Pandas DataFrame of numeric targets indexed
-                by the node ID for that target.
-            shuffle (bool): If True the node_ids will be shuffled at each
-                epoch, if False the node_ids will be processed in order.
+            node_targets: a Pandas DataFrame of node_ids.
 
         Returns:
             A NodeSequence object to use with the Attri2Vec model
-            in Keras methods ``fit_generator``, ``evaluate_generator``,
-            and ``predict_generator``.
+            in the Keras method ``predict_generator``.
 
         """
-        return NodeSequence(
-            self, node_targets.index, node_targets.values, shuffle=shuffle
-        )
+        return NodeSequence(self, node_ids.index)
 
 
 class SparseFullBatchNodeSequence(Sequence):
@@ -1084,7 +1063,7 @@ def flow(self, node_ids, targets=None, shuffle=False):
         supplied node_ids to be used by the downstream task. They should
         be given in the same order as the list of node IDs.
         If they are not specified (for example, for use in prediction),
-        the targets will not be available to the downsteam task.
+        the targets will not be available to the downstream task.
 
         Note that the shuffle argument should be True for training and
         False for prediction.

From 197f4dedcb126c57f22cb7ea1f86f2f51ca03411 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:27:54 +1000
Subject: [PATCH 71/82] update link_mappers.py

---
 stellargraph/mapper/link_mappers.py | 32 +++++++++++------------------
 1 file changed, 12 insertions(+), 20 deletions(-)

diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
index a19d64041..6fc3d14da 100755
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -453,7 +453,7 @@ def flow(self, link_ids, targets=None, shuffle=False):
         supplied link_ids to be used by the downstream task. They should
         be given in the same order as the list of link IDs.
         If they are not specified (for example, for use in prediction),
-        the targets will not be available to the downsteam task.
+        the targets will not be available to the downstream task.
 
         Note that the shuffle argument should be True for training and
         False for prediction.
@@ -631,7 +631,7 @@ def flow(self, link_ids, targets=None, shuffle=False):
         supplied link_ids to be used by the downstream task. They should
         be given in the same order as the list of link IDs.
         If they are not specified (for example, for use in prediction),
-        the targets will not be available to the downsteam task.
+        the targets will not be available to the downstream task.
 
         Note that the shuffle argument should be True for training and
         False for prediction.
@@ -659,7 +659,7 @@ def flow(self, link_ids, targets=None, shuffle=False):
 
 class Attri2VecLinkGenerator:
     """
-    A data generator for link/node pair prediction with attri2vec models
+    A data generator for context node prediction with attri2vec models.
 
     At minimum, supply the StellarGraph and the batch size.
 
@@ -667,7 +667,7 @@ class Attri2VecLinkGenerator:
     machine learning. Currently the model requires node features for all
     nodes in the graph.
 
-    Use the :meth:`.flow` method supplying the nodes and (optionally) targets,
+    Use the :meth:`.flow` method supplying the nodes and targets,
     or an UnsupervisedSampler instance that generates node samples on demand,
     to get an object that can be used as a Keras data generator.
 
@@ -686,6 +686,7 @@ def __init__(self, G, batch_size, name=None):
         if not isinstance(G, StellarGraphBase):
             raise TypeError("Graph must be a StellarGraph object.")
 
+        # Check if the graph has features
         G.check_graph_for_ml(features=True)
 
         self.graph = G
@@ -701,7 +702,7 @@ def sample_features(self, head_links):
             head_links: An iterable of edges to perform sampling for.
 
         Returns:
-            A list of feaure arrys, with each element being the feature of a
+            A list of feature arrays, with each element being the feature of a
             target node and the id of the corresponding context node.
         """
 
@@ -713,22 +714,17 @@ def sample_features(self, head_links):
 
         return batch_feats
 
-    def flow(self, link_ids, targets=None, shuffle=False):
+    def flow(self, link_ids, targets=None):
         """
-        Creates a generator/sequence object for training or evaluation
+        Creates a generator/sequence object for training
         with the supplied edge IDs and numeric targets.
 
-        The edge IDs are the edges to train or inference on. They are
+        The edge IDs are the edges to train. They are
         expected to by tuples of (source_id, destination_id).
 
         The targets are an array of numeric targets corresponding to the
-        supplied link_ids to be used by the downstream task. They should
-        be given in the same order as the list of link IDs.
-        If they are not specified (for example, for use in prediction),
-        the targets will not be available to the downsteam task.
-
-        Note that the shuffle argument should be True for training and
-        False for prediction.
+        supplied link_ids to be used by the attri2vec context node prediction task. 
+        They should be given in the same order as the list of link IDs.
 
         Args:
             link_ids (list or UnsupervisedSampler): an iterable of (src_id, dst_id) tuples
@@ -736,10 +732,6 @@ def flow(self, link_ids, targets=None, shuffle=False):
                 method to generate samples on the fly.
             targets (optional, array): a 2D array of numeric targets with shape
                 `(len(link_ids), target_size)`.
-            shuffle (optional, bool): The value of 'shuffle' is only relevant if 'link_ids' 
-                is an Iterable and it is ignored if the latter is an instance of UnsupervisedSampler. 
-                If True the node_ids will be shuffled at each epoch, if False the node_ids will be 
-                processed in order.
 
         Returns:
             An OnDemandLinkSequence object to use with the attri2vec model.
@@ -750,7 +742,7 @@ def flow(self, link_ids, targets=None, shuffle=False):
 
         # Otherwise pass iterable to standard LinkSequence
         elif isinstance(link_ids, collections.Iterable):
-            return LinkSequence(self, link_ids, targets, shuffle)
+            return LinkSequence(self, link_ids, targets, shuffle=True)
 
         else:
             raise TypeError(

From 34ae2da1f581f5edb6e1c01596854650ccc510d8 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:28:15 +1000
Subject: [PATCH 72/82] update attri2vec.py

---
 stellargraph/layer/attri2vec.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/stellargraph/layer/attri2vec.py b/stellargraph/layer/attri2vec.py
index 3f30e90ae..4360e2c3d 100644
--- a/stellargraph/layer/attri2vec.py
+++ b/stellargraph/layer/attri2vec.py
@@ -36,13 +36,12 @@ class Attri2Vec:
     corresponding to the feature dimensions for each hidden layer and a generator object.
 
     Args:
-        layer_sizes (list): Hidden feature dimensions for each layer
+        layer_sizes (list): Hidden feature dimensions for each layer.
         generator (Sequence): A NodeSequence or LinkSequence. 
         input_dim (int): The dimensions of the node features used as input to the model.
         node_num (int): The number of nodes in the given graph.
         bias (bool): If True a bias vector is learnt for each layer in the attri2vec model, default to False.
-        activation (str): The activation function of each layer in the attri2vec model, which takes values from 
-        "linear", "relu" and "sigmoid"(default).
+        activation (str): The activation function of each layer in the attri2vec model, which takes values from "linear", "relu" and "sigmoid"(default).
         normalize ("l2" or None): The normalization used after each layer, default to None.
 
     """
@@ -148,7 +147,7 @@ def node_model(self):
 
     def link_model(self):
         """
-        Builds a Attri2Vec model for link or node pair prediction
+        Builds a Attri2Vec model for context node prediction
 
         Returns:
             tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors for (src, dst) nodes in the node pairs

From a97ed363ed65a52ed83269d47ac67f40a95b2924 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:28:47 +1000
Subject: [PATCH 73/82] delete attri2vec DBLP node classification demo

---
 ...vec-DBLP-node-classification-example.ipynb | 747 ------------------
 1 file changed, 747 deletions(-)
 delete mode 100644 demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb

diff --git a/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb
deleted file mode 100644
index 82df33956..000000000
--- a/demos/node-classification/attri2vec/attri2vec-DBLP-node-classification-example.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Node Representation Learning with attri2vec on DBLP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
-    "\n",
-    "\n",
-    "## attri2vec\n",
-    "\n",
-    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
-    "\n",
-    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
-    "\n",
-    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
-    "\n",
-    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "import stellargraph as sg\n",
-    "from stellargraph.data import UnsupervisedSampler\n",
-    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
-    "from stellargraph.layer import Attri2Vec, link_classification\n",
-    "\n",
-    "from tensorflow import keras\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
-    "from sklearn.metrics import accuracy_score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading DBLP network data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This demo uses a DBLP citation network, a subgraph extracted from [DBLP-Citation-network V3](https://aminer.org/citation). To form this subgraph, papers from four subjects are extracted according to their venue information: *Database, Data Mining, Artificial Intelligence and Computer Vision*, and papers with no citations are removed. The DBLP network contains 18,448 papers and 45,661 citation relations. From paper titles, we construct 2,476-dimensional binary node feature vectors, with each element indicating the presence/absence of the corresponding word. By ignoring the citation direction, we take the DBLP subgraph as an undirected network.\n",
-    "\n",
-    "The dataset used in this demo can be downloaded from https://www.kaggle.com/daozhang/dblp-subgraph.\n",
-    "The following is the description of the dataset:\n",
-    "\n",
-    "> The content.txt file contains descriptions of the papers in the following format:\n",
-    "\n",
-    " \t\t<paper_id> <word_attributes> <class_label> <publication_year>\n",
-    "        \n",
-    "> The first entry in each line contains the unique integer ID (ranging from 0 to 18,447) of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last two entries in the line are the class label and the publication year of the paper.\n",
-    "> The edgeList.txt file contains the citation relations. Each line describes a link in the following format:\n",
-    "\t\t\n",
-    "        <ID of paper1> <ID of paper2>\n",
-    "        \n",
-    "> Each line contains two paper IDs, with paper2 citing paper1 or paper1 citing paper2.\n",
-    "\n",
-    "\n",
-    "Download and unzip the dblp-subgraph.zip file to a location on your computer and set the `data_dir` variable to\n",
-    "point to the location of the dataset (the \"DBLP\" directory containing \"content.txt\" and \"edgeList.txt\")."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dir = \"~/data/DBLP\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load the graph from the edgelist."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edgelist = pd.read_csv(os.path.join(data_dir, \"edgeList.txt\"), sep='\\t', header=None, names=[\"source\", \"target\"])\n",
-    "edgelist[\"label\"] = \"cites\"  # set the edge type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load paper content features, subjects and publishing years."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_names = [\"w_{}\".format(ii) for ii in range(2476)]\n",
-    "node_column_names =  feature_names + [\"subject\", \"year\"]\n",
-    "node_data = pd.read_csv(os.path.join(data_dir, \"content.txt\"), sep='\\t', header=None, names=node_column_names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Construct the graph from edge list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify node types."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nx.set_node_attributes(G_nx, \"paper\", \"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_features = node_data[feature_names]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the Stellargraph with node features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = sg.StellarGraph(G_nx, node_features=node_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 18448, Edges: 45611\n",
-      "\n",
-      " Node types:\n",
-      "  paper: [18448]\n",
-      "    Edge types: paper-cites->paper\n",
-      "\n",
-      " Edge types:\n",
-      "    paper-cites->paper: [45611]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(G.info())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train attri2vec on the DBLP Subgraph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes = list(G.nodes())\n",
-    "number_of_walks = 4\n",
-    "length = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unsupervised_samples = UnsupervisedSampler(G, nodes=nodes, length=length, number_of_walks=number_of_walks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set the batch size and the number of epochs. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 50\n",
-    "epochs = 8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Attri2VecLinkGenerator with an estimated 14758 batches generated on the fly per epoch.\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_gen = Attri2VecLinkGenerator(G, batch_size).flow(unsupervised_samples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
-    "\n",
-    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "layer_sizes = [128]\n",
-    "attri2vec = Attri2Vec(\n",
-    "    layer_sizes=layer_sizes, generator=train_gen, bias=False, normalize=None\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 16:19:56.064584 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 16:19:56.085483 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
-    "x_inp, x_out = attri2vec.build()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
-     ]
-    }
-   ],
-   "source": [
-    "prediction = link_classification(\n",
-    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method='ip'\n",
-    ")(x_out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
-    "    loss=keras.losses.binary_crossentropy,\n",
-    "    metrics=[keras.metrics.binary_accuracy],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Train the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/8\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "W0925 16:19:56.485485 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14759/14759 [==============================] - 371s 25ms/step - loss: 0.7013 - binary_accuracy: 0.5400\n",
-      "Epoch 2/8\n",
-      "14759/14759 [==============================] - 373s 25ms/step - loss: 0.6139 - binary_accuracy: 0.6296\n",
-      "Epoch 3/8\n",
-      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.5194 - binary_accuracy: 0.7376\n",
-      "Epoch 4/8\n",
-      "14759/14759 [==============================] - 376s 25ms/step - loss: 0.4491 - binary_accuracy: 0.7930\n",
-      "Epoch 5/8\n",
-      "14759/14759 [==============================] - 378s 26ms/step - loss: 0.4003 - binary_accuracy: 0.8237\n",
-      "Epoch 6/8\n",
-      "14759/14759 [==============================] - 382s 26ms/step - loss: 0.3669 - binary_accuracy: 0.8434\n",
-      "Epoch 7/8\n",
-      "14759/14759 [==============================] - 380s 26ms/step - loss: 0.3407 - binary_accuracy: 0.8566\n",
-      "Epoch 8/8\n",
-      "14759/14759 [==============================] - 379s 26ms/step - loss: 0.3204 - binary_accuracy: 0.8673\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit_generator(\n",
-    "    train_gen,\n",
-    "    epochs=epochs,\n",
-    "    verbose=1,\n",
-    "    use_multiprocessing=False,\n",
-    "    workers=1,\n",
-    "    shuffle=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Visualise Node Embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_inp_src = x_inp[0]\n",
-    "x_out_src = x_out[0]\n",
-    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the node embeddings by applying the learned mapping function to node content features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "369/369 [==============================] - 1s 2ms/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "node_ids = node_data.index\n",
-    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
-    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get node subjects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "node_targets = [ node_data[\"subject\"][node_id] for node_id in node_ids ]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Transform the embeddings to 2d space for visualisation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform = TSNE # PCA\n",
-    "\n",
-    "trans = transform(n_components=2)\n",
-    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# draw the embedding points, coloring them by the target label (paper subject)\n",
-    "alpha = 0.7\n",
-    "label_map = { l: i for i, l in enumerate(np.unique(node_targets)) }\n",
-    "node_colours = [ label_map[target] for target in node_targets ]\n",
-    "\n",
-    "plt.figure(figsize=(7,7))\n",
-    "plt.axes().set(aspect=\"equal\")\n",
-    "plt.scatter(node_embeddings_2d[:,0], \n",
-    "            node_embeddings_2d[:,1], \n",
-    "            c=node_colours, cmap=\"jet\", alpha=alpha)\n",
-    "plt.title('{} visualization of node embeddings'.format(transform.__name__))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Node Classification Task"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The embeddings learned by `attri2vec` can be used as feature vectors in downstream tasks, such as node classification and link prediction.\n",
-    "\n",
-    "In this example, we will use the `attri2vec` node embeddings to train a classifier to predict the subject of a paper in DBLP."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# X will hold the 128-dimensional input features\n",
-    "X = node_embeddings  \n",
-    "# y holds the corresponding target values\n",
-    "y = np.array(node_targets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Splitting\n",
-    "\n",
-    "We split the data into train and test sets. \n",
-    "\n",
-    "We use 10% of the data for training and the remaining 90% for testing as a hold out test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Array shapes:\n",
-      " X_train = (1844, 128)\n",
-      " y_train = (1844,)\n",
-      " X_test = (16604, 128)\n",
-      " y_test = (16604,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.1, test_size=None)\n",
-    "print(\"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Classifier Training\n",
-    "\n",
-    "We train a Logistic Regression classifier on the training data. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
-       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
-       "                     max_iter=2000, multi_class='ovr', n_jobs=None,\n",
-       "                     penalty='l2', random_state=None, refit=True,\n",
-       "                     scoring='accuracy', solver='lbfgs', tol=0.0001,\n",
-       "                     verbose=False)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clf = LogisticRegressionCV(\n",
-    "    Cs=10, \n",
-    "    cv=10, \n",
-    "    scoring=\"accuracy\",\n",
-    "    verbose=False,\n",
-    "    multi_class='ovr',\n",
-    "    max_iter=2000\n",
-    ")\n",
-    "clf.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Predict the hold-out test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = clf.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculate the accuracy of the classifier on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8048662972777644"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(y_test, y_pred)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 63f0a3b95fec5dbbc55a19cd629be4ad03fa04ed Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 16:29:05 +1000
Subject: [PATCH 74/82] update attri2vec citeseer node classification demo

---
 ...citeseer-node-classification-example.ipynb | 36 +++++++++++++------
 1 file changed, 25 insertions(+), 11 deletions(-)

diff --git a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
index ccdf2853a..30f7083ff 100644
--- a/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
+++ b/demos/node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb
@@ -416,10 +416,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0925 17:39:33.838998 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W1001 15:56:58.801946 140735704433600 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W0925 17:39:33.886423 140736388883392 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "W1001 15:56:58.822957 140735704433600 deprecation.py:506] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
      ]
@@ -492,11 +492,18 @@
     "scrolled": true
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/4\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "W0925 17:39:34.398716 140736388883392 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "W1001 15:56:59.080938 140735704433600 deprecation.py:323] From /anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
      ]
@@ -505,14 +512,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/4\n",
-      "1688/1688 [==============================] - 20s 12ms/step - loss: 0.6833 - binary_accuracy: 0.5538\n",
+      "1688/1688 [==============================] - 13s 8ms/step - loss: 0.6828 - binary_accuracy: 0.5543\n",
       "Epoch 2/4\n",
-      "1688/1688 [==============================] - 18s 11ms/step - loss: 0.5183 - binary_accuracy: 0.7489\n",
+      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.5131 - binary_accuracy: 0.7586\n",
       "Epoch 3/4\n",
-      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.3391 - binary_accuracy: 0.8740\n",
+      "1688/1688 [==============================] - 11s 6ms/step - loss: 0.3334 - binary_accuracy: 0.8769\n",
       "Epoch 4/4\n",
-      "1688/1688 [==============================] - 18s 10ms/step - loss: 0.2485 - binary_accuracy: 0.9145\n"
+      "1688/1688 [==============================] - 11s 7ms/step - loss: 0.2462 - binary_accuracy: 0.9157\n"
      ]
     }
    ],
@@ -568,7 +574,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "43/43 [==============================] - 0s 3ms/step\n"
+      "43/43 [==============================] - 0s 2ms/step\n"
      ]
     }
    ],
@@ -619,7 +625,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -720,6 +726,14 @@
    "execution_count": 30,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 9 members, which is too few. The minimum number of members in any class cannot be less than n_splits=10.\n",
+      "  % (min_groups, self.n_splits)), Warning)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -778,7 +792,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7498683517640864"
+       "0.7546076882569773"
       ]
      },
      "execution_count": 32,

From 5d1fb251101029648c7e9f82d44c16e253a82c72 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 17:45:14 +1000
Subject: [PATCH 75/82] update test_stellargraph.py by adding test for the
 get_index_for_nodes function

---
 tests/core/test_stellargraph.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/tests/core/test_stellargraph.py b/tests/core/test_stellargraph.py
index e0cfdb2ad..596e2df42 100644
--- a/tests/core/test_stellargraph.py
+++ b/tests/core/test_stellargraph.py
@@ -219,6 +219,24 @@ def test_digraph_schema():
     assert schema.get_edge_type((4, 0, 0)) == ("user", "rating", "movie")
     with pytest.raises(IndexError):
         schema.get_edge_type((0, 4, 0))
+        
+        
+def test_get_index_for_nodes():
+    sg = example_stellar_graph_1(feature_name="feature", feature_size=8)
+    aa = sg.get_index_for_nodes([1, 2, 3, 4])
+    assert aa == [0, 1, 2, 3]
+    
+    sg = example_hin_1(feature_name="feature")
+    aa = sg.get_index_for_nodes([0, 1, 2, 3])
+    assert aa == [0, 1, 2, 3]
+    aa = sg.get_index_for_nodes([0, 1, 2, 3], "A")
+    assert aa == [0, 1, 2, 3]
+    aa = sg.get_index_for_nodes([4, 5, 6])
+    assert aa == [0, 1, 2]
+    aa = sg.get_index_for_nodes([4, 5, 6], "B")
+    assert aa == [0, 1, 2]
+    with pytest.raises(ValueError):
+        aa = sg.get_index_for_nodes([1, 2, 5])
 
 
 def test_feature_conversion_from_nodes():

From 6012994a14223f9a3976d1d52ced2e9a4d4950ea Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 17:50:07 +1000
Subject: [PATCH 76/82] update test_node_mappers.py

---
 tests/mapper/test_node_mappers.py | 72 +------------------------------
 1 file changed, 2 insertions(+), 70 deletions(-)

diff --git a/tests/mapper/test_node_mappers.py b/tests/mapper/test_node_mappers.py
index 9002bf32b..f7b7ef2b2 100755
--- a/tests/mapper/test_node_mappers.py
+++ b/tests/mapper/test_node_mappers.py
@@ -751,88 +751,20 @@ def test_attri2vec_nodemapper_1():
         Attri2VecNodeGenerator(G1, batch_size=2).flow(["A", "B"])
 
 
-def test_attri2vec_nodemapper_shuffle():
+def test_attri2vec_nodemapper_2():
     n_feat = 1
     n_batch = 2
 
     G = example_graph_2(feature_size=n_feat)
     nodes = list(G.nodes())
 
-    # With shuffle
-    random.seed(15)
-    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
-        nodes, nodes, shuffle=True
-    )
-
-    expected_node_batches = [[5, 4], [3, 1], [2]]
-    assert len(mapper) == 3
-    for ii in range(len(mapper)):
-        nf, nl = mapper[ii]
-        assert all(np.ravel(nf) == expected_node_batches[ii])
-        assert all(np.array(nl) == expected_node_batches[ii])
-
-    # This should re-shuffle the IDs
-    mapper.on_epoch_end()
-    expected_node_batches = [[4, 3], [1, 5], [2]]
-    assert len(mapper) == 3
-    for ii in range(len(mapper)):
-        nf, nl = mapper[ii]
-        assert all(np.ravel(nf) == expected_node_batches[ii])
-        assert all(np.array(nl) == expected_node_batches[ii])
-
     # With no shuffle
-    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(
-        nodes, nodes, shuffle=False
-    )
+    mapper = Attri2VecNodeGenerator(G, batch_size=n_batch).flow(nodes)
     expected_node_batches = [[1, 2], [3, 4], [5]]
     assert len(mapper) == 3
     for ii in range(len(mapper)):
         nf, nl = mapper[ii]
         assert all(np.ravel(nf) == expected_node_batches[ii])
-        assert all(np.array(nl) == expected_node_batches[ii])
-
-
-def test_attri2vec_nodemapper_with_labels():
-    n_feat = 4
-    n_batch = 2
-
-    # test graph
-    G2 = example_graph_2(n_feat)
-    nodes = list(G2)
-    labels = [n * 2 for n in nodes]
-
-    gen = Attri2VecNodeGenerator(G2, batch_size=n_batch).flow(nodes, labels)
-    assert len(gen) == 3
-
-    for ii in range(3):
-        nf, nl = gen[ii]
-
-        # Check sizes - note batch sizes are (2,2,1) for each iteration
-        assert nf.shape[1:] == (n_feat,)
-
-        # Check labels
-        assert all(int(a) == int(2 * b) for a, b in zip(nl, nf[:, 0]))
-
-    # Check beyond the graph lengh
-    with pytest.raises(IndexError):
-        nf, nl = gen[len(gen)]
-
-
-def test_attri2vec_nodemapper_incorrect_targets():
-    """
-    Tests checks on target shape
-    """
-    n_feat = 4
-    n_batch = 2
-
-    # test graph
-    G = example_graph_1(feature_size=n_feat)
-
-    with pytest.raises(TypeError):
-        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), 1)
-
-    with pytest.raises(ValueError):
-        Attri2VecNodeGenerator(G, batch_size=n_batch).flow(list(G), targets=[])
 
 
 def create_graph_features():

From 6ef707ad5bd0d57daa7d8ad60fa4b6d66a098cf3 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 17:50:22 +1000
Subject: [PATCH 77/82] update test_link_mappers.py

---
 tests/mapper/test_link_mappers.py | 40 ++++++++++++++-----------------
 1 file changed, 18 insertions(+), 22 deletions(-)

diff --git a/tests/mapper/test_link_mappers.py b/tests/mapper/test_link_mappers.py
index 8442cd088..f04b94ace 100755
--- a/tests/mapper/test_link_mappers.py
+++ b/tests/mapper/test_link_mappers.py
@@ -747,30 +747,26 @@ def test_Attri2VecLinkGenerator_1(self):
         with pytest.raises(IndexError):
             nf, nl = mapper[2]
 
-    def test_Attri2VecLinkGenerator_shuffle(self):
-        def test_edge_consistency(shuffle):
-            G = example_Graph_2(1)
-            edges = list(G.edges())
-            nodes = list(G.nodes())
-            edge_labels = list(range(len(edges)))
-
-            mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
-                edges, edge_labels, shuffle=shuffle
-            )
-
-            assert len(mapper) == 2
+    def test_edge_consistency(self):
+        G = example_Graph_2(1)
+        edges = list(G.edges())
+        nodes = list(G.nodes())
+        edge_labels = list(range(len(edges)))
+
+        mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
+            edges, edge_labels
+        )
 
-            for batch in range(len(mapper)):
-                nf, nl = mapper[batch]
-                e1 = edges[nl[0]]
-                e2 = edges[nl[1]]
-                assert nf[0][0, 0] == e1[0]
-                assert nf[1][0] == nodes.index(e1[1])
-                assert nf[0][1, 0] == e2[0]
-                assert nf[1][1] == nodes.index(e2[1])
+        assert len(mapper) == 2
 
-        test_edge_consistency(True)
-        test_edge_consistency(False)
+        for batch in range(len(mapper)):
+            nf, nl = mapper[batch]
+            e1 = edges[nl[0]]
+            e2 = edges[nl[1]]
+            assert nf[0][0, 0] == e1[0]
+            assert nf[1][0] == nodes.index(e1[1])
+            assert nf[0][1, 0] == e2[0]
+            assert nf[1][1] == nodes.index(e2[1])
 
     def test_Attri2VecLinkGenerator_not_Stellargraph(self):
         G = nx.Graph()

From 6c35c6b7753f3ff1ebb6a59f2d1d65c6edf4392a Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 17:50:35 +1000
Subject: [PATCH 78/82] update node_mappers.py

---
 stellargraph/mapper/node_mappers.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/stellargraph/mapper/node_mappers.py b/stellargraph/mapper/node_mappers.py
index 50e076ec2..30cb39ba1 100755
--- a/stellargraph/mapper/node_mappers.py
+++ b/stellargraph/mapper/node_mappers.py
@@ -591,7 +591,7 @@ def sample_features(self, head_nodes):
 
     def flow(self, node_ids):
         """
-        Creates a generator/sequence object for evaluation
+        Creates a generator/sequence object for node representation prediction
         with the supplied node ids.
 
         The node IDs are the nodes to inference on: the embeddings
@@ -610,11 +610,11 @@ def flow(self, node_ids):
 
     def flow_from_dataframe(self, node_ids):
         """
-        Creates a generator/sequence object for training or evaluation
+        Creates a generator/sequence object for node representation prediction
         with the supplied node ids.
 
         Args:
-            node_targets: a Pandas DataFrame of node_ids.
+            node_ids: a Pandas DataFrame of node_ids.
 
         Returns:
             A NodeSequence object to use with the Attri2Vec model

From 2e168e55bad1de6d073a0c3ebbbc4d5f8e35feb2 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 17:50:46 +1000
Subject: [PATCH 79/82] update link_mappers.py

---
 stellargraph/mapper/link_mappers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/stellargraph/mapper/link_mappers.py b/stellargraph/mapper/link_mappers.py
index 6fc3d14da..cdaaa9228 100755
--- a/stellargraph/mapper/link_mappers.py
+++ b/stellargraph/mapper/link_mappers.py
@@ -659,7 +659,7 @@ def flow(self, link_ids, targets=None, shuffle=False):
 
 class Attri2VecLinkGenerator:
     """
-    A data generator for context node prediction with attri2vec models.
+    A data generator for context node prediction with the attri2vec model.
 
     At minimum, supply the StellarGraph and the batch size.
 
@@ -674,7 +674,7 @@ class Attri2VecLinkGenerator:
     Example::
 
         G_generator = Attri2VecLinkGenerator(G, 50)
-        train_data_gen = G_generator.flow(edge_ids)
+        train_data_gen = G_generator.flow(edge_ids, edge_labels)
 
     Args:
         G (StellarGraph): A machine-learning ready graph.

From 26a0eadacbdf006039b1679cae9d9edc7dcad501 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 17:51:00 +1000
Subject: [PATCH 80/82] update attri2vec.py

---
 stellargraph/layer/attri2vec.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/stellargraph/layer/attri2vec.py b/stellargraph/layer/attri2vec.py
index 4360e2c3d..c4d7b2b72 100644
--- a/stellargraph/layer/attri2vec.py
+++ b/stellargraph/layer/attri2vec.py
@@ -30,7 +30,7 @@
 class Attri2Vec:
     """
     Implementation of the attri2vec algorithm of Zhang et al. with Keras layers.
-    see: https://arxiv.org/abs/1901.04095
+    see: https://arxiv.org/abs/1901.04095.
 
     The model minimally requires specification of the layer sizes as a list of ints
     corresponding to the feature dimensions for each hidden layer and a generator object.
@@ -129,7 +129,7 @@ def __call__(self, xin):
 
     def node_model(self):
         """
-        Builds a Attri2Vec model for node prediction
+        Builds a Attri2Vec model for node representation prediction.
 
         Returns:
             tuple: (x_inp, x_out) where ``x_inp`` is a Keras input tensor
@@ -147,7 +147,7 @@ def node_model(self):
 
     def link_model(self):
         """
-        Builds a Attri2Vec model for context node prediction
+        Builds a Attri2Vec model for context node prediction.
 
         Returns:
             tuple: (x_inp, x_out) where ``x_inp`` is a list of Keras input tensors for (src, dst) nodes in the node pairs
@@ -174,13 +174,12 @@ def link_model(self):
 
     def build(self):
         """
-        Builds a Attri2Vec model for node or link/node pair prediction, depending on the generator used to construct
-        the model (whether it is a node or link/node pair generator).
+        Builds a Attri2Vec model for context node prediction.
 
         Returns:
             tuple: (x_inp, x_out), where ``x_inp`` contains Keras input tensor(s)
-            for the specified Attri2Vec model (either node or link/node pair model) and ``x_out`` contains
-            model output tensor(s) of shape (batch_size, layer_sizes[-1])
+            for the Attri2Vec model to perform context node prediction and ``x_out`` contains
+            model output tensor(s) of shape (batch_size, layer_sizes[-1]).
 
         """
         return self.link_model()

From 2b290bbb079f9b58567105830cddda55febdf2e9 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 17:53:57 +1000
Subject: [PATCH 81/82] update test_link_mappers.py

---
 tests/mapper/test_link_mappers.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/tests/mapper/test_link_mappers.py b/tests/mapper/test_link_mappers.py
index f04b94ace..a06c34b6e 100755
--- a/tests/mapper/test_link_mappers.py
+++ b/tests/mapper/test_link_mappers.py
@@ -753,9 +753,7 @@ def test_edge_consistency(self):
         nodes = list(G.nodes())
         edge_labels = list(range(len(edges)))
 
-        mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(
-            edges, edge_labels
-        )
+        mapper = Attri2VecLinkGenerator(G, batch_size=2).flow(edges, edge_labels)
 
         assert len(mapper) == 2
 

From e70c5578585fc6a4f454902f50a8e6b1632f11b0 Mon Sep 17 00:00:00 2001
From: Daokun Zhang <daokunzhang2015@gmail.com>
Date: Tue, 1 Oct 2019 18:18:26 +1000
Subject: [PATCH 82/82] update test_stellargraph.py with black format

---
 tests/core/test_stellargraph.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/core/test_stellargraph.py b/tests/core/test_stellargraph.py
index 596e2df42..0b05932ca 100644
--- a/tests/core/test_stellargraph.py
+++ b/tests/core/test_stellargraph.py
@@ -219,13 +219,13 @@ def test_digraph_schema():
     assert schema.get_edge_type((4, 0, 0)) == ("user", "rating", "movie")
     with pytest.raises(IndexError):
         schema.get_edge_type((0, 4, 0))
-        
-        
+
+
 def test_get_index_for_nodes():
     sg = example_stellar_graph_1(feature_name="feature", feature_size=8)
     aa = sg.get_index_for_nodes([1, 2, 3, 4])
     assert aa == [0, 1, 2, 3]
-    
+
     sg = example_hin_1(feature_name="feature")
     aa = sg.get_index_for_nodes([0, 1, 2, 3])
     assert aa == [0, 1, 2, 3]