Model | Paper | Note |
---|---|---|
DeepWalk | [KDD 2014]DeepWalk: Online Learning of Social Representations | 【Graph Embedding】DeepWalk:算法原理,实现和应用 |
LINE | [WWW 2015]LINE: Large-scale Information Network Embedding | 【Graph Embedding】LINE:算法原理,实现和应用 |
Node2Vec | [KDD 2016]node2vec: Scalable Feature Learning for Networks | 【Graph Embedding】Node2Vec:算法原理,实现和应用 |
SDNE | [KDD 2016]Structural Deep Network Embedding | 【Graph Embedding】SDNE:算法原理,实现和应用 |
Struc2Vec | [KDD 2017]struc2vec: Learning Node Representations from Structural Identity | 【Graph Embedding】Struc2Vec:算法原理,实现和应用 |
- clone the repo and make sure you have installed
tensorflow
ortensorflow-gpu
on your local machine. - run following commands
python setup.py install
cd examples
python deepwalk_wiki.py
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])# Read graph
model = DeepWalk(G,walk_length=10,num_walks=80,workers=1)#init model
model.train(window_size=5,iter=3)# train model
embeddings = model.get_embeddings()# get embedding vectors
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = LINE(G,embedding_size=128,order='second') #init model,order can be ['first','second','all']
model.train(batch_size=1024,epochs=50,verbose=2)# train model
embeddings = model.get_embeddings()# get embedding vectors
G=nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
create_using = nx.DiGraph(), nodetype = None, data = [('weight', int)])#read graph
model = Node2Vec(G, walk_length = 10, num_walks = 80,p = 0.25, q = 4, workers = 1)#init model
model.train(window_size = 5, iter = 3)# train model
embeddings = model.get_embeddings()# get embedding vectors
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = SDNE(G,hidden_size=[256,128]) #init model
model.train(batch_size=3000,epochs=40,verbose=2)# train model
embeddings = model.get_embeddings()# get embedding vectors
G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
model.train(window_size = 5, iter = 3)# train model
embeddings = model.get_embeddings()# get embedding vectors
Tensorflow implementation of Self-Paced Network Embedding
A tensorflow re-implementation of Self-Paced Network Embedding,use random walk to get positive pair
python 3.6, tensorflow 1.12.0
To run the codes, use: python seedne_new.py
on dataset cora,F1-micro:0.78,F1-macro:0.77 see SeedNE_new-Thu-May-16-10-22-56-2019-log.txt or result.png
Code for the AAAI 2018 paper "HARP: Hierarchical Representation Learning for Networks". HARP is a meta-strategy to improve several state-of-the-art network embedding algorithms, such as DeepWalk, LINE and Node2vec.
You can read the preprint of our paper on Arxiv.
This code run with Python 2.
The following Python packages are required to install HARP.
magicgraph is a library for processing graph data. To install, run the following commands:
git clone https://github.com/phanein/magic-graph.git
cd magic-graph
python setup.py install
Then, install HARP and the other requirements:
git clone https://github.com/GTmac/HARP.git
cd HARP
pip install -r requirements.txt
To run HARP on the CiteSeer dataset using LINE as the underlying network embedding model, run the following command:
python src/harp.py --input example_graphs/citeseer/citeseer.mat --model line --output citeseer.npy --sfdp-path bin/sfdp_linux
Parameters available:
--input: input_filename
-
--format mat
for a Matlab .mat file containing an adjacency matrix. By default, the variable name of the adjacency matrix isnetwork
; you can also specify it with--matfile-variable-name
. -
--format adjlist
for an adjacency list, e.g:1 2 3 4 5 6 7 8 9 11 12 13 14 18 20 22 32
2 1 3 4 8 14 18 20 22 31
3 1 2 4 8 9 10 14 28 29 33
...
-
--format edgelist
for an edge list, e.g:1 2
1 3
1 4
2 5
...
--output: output_filename
The output representations in Numpy .npy
format.
Note that we assume the nodes in your input file are indexed from 0 to N - 1.
--model model_name
The underlying network embeddings model to use. Could be deepwalk
, line
or node2vec
.
Note that node2vec
uses the default parameters, which is p=1.0 and q=1.0.
--sfdp-path sfdp_path
Path to the binary file of SFDP, which is the module we used for graph coarsening.
You can set it to sfdp_linux
, sfdp_osx
or sfdp_windows.exe
depending on your operating system.
More options: The full list of command line options is available with python src/harp.py --help
.
To evaluate the embeddings on a multi-label classification task, run the following command:
python src/scoring.py -e citeseer.npy -i example_graphs/citeseer/citeseer.mat -t 1 2 3 4 5 6 7 8 9
Where -e
specifies the embeddings file, -i
specifies the .mat
file containing node labels,
and -t
specifies the list of training example ratios to use.
SFDP is a library for multi-level graph drawing, which is a part of GraphViz.
We use SFDP for graph coarsening in this implementation.
Note that SFDP is included as a binary file under /bin
;
please choose the proper binary file according to your operation system.
Currently we have the binary files under OSX, Linux and Windows.
If you find HARP useful in your research, please cite our paper:
@inproceedings{harp,
title={HARP: Hierarchical Representation Learning for Networks},
author={Chen, Haochen and Perozzi, Bryan and Hu, Yifan and Skiena, Steven},
booktitle={Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence},
year={2018},
organization={AAAI Press}
}
GraphGAN: Graph Representation Learning With Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
32nd AAAI Conference on Artificial Intelligence, 2018
GraphGAN unifies two schools of graph representation learning methodologies: generative methods and discriminative methods, via adversarial training in a minimax game. The generator is guided by the signals from the discriminator and improves its generating performance, while the discriminator is pushed by the generator to better distinguish ground truth from generated samples.
data/
: training and test datapre_train/
: pre-trained node embeddingsNote: the dimension of pre-trained node embeddings should equal n_emb in src/GraphGAN/config.py
results/
: evaluation results and the learned embeddings of the generator and the discriminatorsrc/
: source codes
The code has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):
- tensorflow == 1.8.0
- tqdm == 4.23.4 (for displaying the progress bar)
- numpy == 1.14.3
- sklearn == 0.19.1
The input data should be an undirected graph in which node IDs start from 0 to N-1 (N is the number of nodes in the graph). Each line contains two node IDs indicating an edge in the graph.
0 1
3 2
...
mkdir cache
cd src/GraphGAN
python graph_gan.py