Rearrange demos to have fewer folders

.buildkite/pipeline.yml

@@ -83,7 +83,7 @@ steps:
     <<: *timeout
     key: "test-notebooks"
     depends_on: "runner-3_6"
-    parallelism: 46
+    parallelism: 47
     command: ".buildkite/steps/test-demo-notebooks.sh"
     plugins:
       <<: *plugins

.buildkite/steps/test-demo-notebooks.sh

@@ -32,8 +32,8 @@ fi
 f=${NOTEBOOKS[$INDEX]}
 
 case $(basename "$f") in
-  'attacks_clustering_analysis.ipynb' | 'hateful-twitters-interpretability.ipynb' | 'hateful-twitters.ipynb' | 'stellargraph-attri2vec-DBLP.ipynb' | \
-    'node-link-importance-demo-gat.ipynb' | 'node-link-importance-demo-gcn.ipynb' | 'node-link-importance-demo-gcn-sparse.ipynb' | 'rgcn-aifb-node-classification-example.ipynb' | \
+  'attacks_clustering_analysis.ipynb' | 'hateful-twitters-interpretability.ipynb' | 'hateful-twitters.ipynb' | 'attri2vec-link-prediction.ipynb' | \
+    'gat-node-link-importance.ipynb' | 'gcn-node-link-importance.ipynb' | 'gcn-sparse-node-link-importance.ipynb' | 'rgcn-node-classification.ipynb' | \
     'stellargraph-metapath2vec.ipynb')
     # These notebooks do not yet work on CI:
     # FIXME #818: datasets can't be downloaded

README.md

@@ -166,7 +166,7 @@ print(f"Test set: loss = {loss}, accuracy = {accuracy}")
 
 This algorithm is spelled out in more detail in [its extended narrated notebook][gcn-demo]. We provide [many more algorithms, each with a detailed example][demos].
 
-[gcn-demo]: https://stellargraph.readthedocs.io/en/latest/demos/node-classification/gcn/gcn-cora-node-classification-example.html
+[gcn-demo]: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-node-classification.html
 
 ## Algorithms
 The StellarGraph library currently includes the following algorithms for graph machine learning:

demos/README.md

@@ -33,14 +33,14 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td>see T-GCN</td>
     <td>yes</td>
-    <td><a href='node-classification/gcn/gcn-cora-node-classification-example.ipynb'>demo</a></td>
-    <td><a href='link-prediction/gcn/cora-gcn-links-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/gcn-node-classification.ipynb'>demo</a></td>
+    <td><a href='link-prediction/gcn-link-prediction.ipynb'>demo</a></td>
     <td>
       <span title='UnsupervisedSampler'>US</span>
-      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-cora.ipynb'>DGI</a></span>
+      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-embeddings.ipynb'>DGI</a></span>
     </td>
     <td>see Cluster-GCN</td>
-    <td><a href='graph-classification/supervised-graph-classification.ipynb'>demo</a></td>
+    <td><a href='graph-classification/gcn-supervised-graph-classification.ipynb'>demo</a></td>
   </tr>
   <tr>
     <td>Cluster-GCN</td>
@@ -49,7 +49,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/cluster-gcn/cluster-gcn-node-classification.ipynb'>demo</a></td>
+    <td><a href='node-classification/cluster-gcn-node-classification.ipynb'>demo</a></td>
     <td>yes</td>
     <td></td>
     <td>yes</td>
@@ -62,7 +62,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/rgcn/rgcn-aifb-node-classification-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/rgcn-node-classification.ipynb'>demo</a></td>
     <td>yes</td>
     <td></td>
     <td></td>
@@ -75,7 +75,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td>node features</td>
     <td>time series, sequence</td>
-    <td><a href='time-series/gcn-lstm-LA.ipynb'>demo</a></td>
+    <td><a href='time-series/gcn-lstm-time-series.ipynb'>demo</a></td>
     <td></td>
     <td></td>
     <td></td>
@@ -88,11 +88,11 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/gat/gat-cora-node-classification-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/gat-node-classification.ipynb'>demo</a></td>
     <td>yes</td>
     <td>
       <span title='UnsupervisedSampler'>US</span>
-      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-cora.ipynb'>DGI</a></span>
+      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-embeddings.ipynb'>DGI</a></span>
     </td>
     <td></td>
     <td></td>
@@ -104,7 +104,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/sgc/sgc-node-classification-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/sgc-node-classification.ipynb'>demo</a></td>
     <td>yes</td>
     <td></td>
     <td></td>
@@ -117,7 +117,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/ppnp/ppnp-cora-node-classification-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/ppnp-node-classification.ipynb'>demo</a></td>
     <td>yes</td>
     <td>
       <span title='UnsupervisedSampler'>US</span>
@@ -133,11 +133,11 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/ppnp/ppnp-cora-node-classification-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/ppnp-node-classification.ipynb'>demo</a></td>
     <td>yes</td>
     <td>
       <span title='UnsupervisedSampler'>US</span>
-      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-cora.ipynb'>DGI</a></span>
+      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-embeddings.ipynb'>DGI</a></span>
     </td>
     <td></td>
     <td></td>
@@ -151,7 +151,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
-    <td><a href='embeddings/graphwave-barbell.ipynb'>demo</a></td>
+    <td><a href='embeddings/graphwave-embeddings.ipynb'>demo</a></td>
     <td></td>
     <td></td>
   </tr>
@@ -162,26 +162,26 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/attri2vec/attri2vec-citeseer-node-classification-example.ipynb'>demo</a></td>
-    <td><a href='link-prediction/attri2vec/stellargraph-attri2vec-DBLP.ipynb'>demo</a></td>
-    <td><a href='embeddings/stellargraph-attri2vec-citeseer.ipynb'>demo</a></td>
+    <td><a href='node-classification/attri2vec-node-classification.ipynb'>demo</a></td>
+    <td><a href='link-prediction/attri2vec-link-prediction.ipynb'>demo</a></td>
+    <td><a href='embeddings/attri2vec-embeddings.ipynb'>demo</a></td>
     <td></td>
     <td></td>
   </tr>
   <tr>
     <td>GraphSAGE</td>
     <td>see HinSAGE</td>
-    <td><a href='node-classification/graphsage/directed-graphsage-on-cora-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/directed-graphsage-node-classification.ipynb'>demo</a></td>
     <td></td>
     <td></td>
     <td>yes</td>
-    <td><a href='node-classification/graphsage/graphsage-cora-node-classification-example.ipynb'>demo</a></td>
-    <td><a href='link-prediction/graphsage/cora-links-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/graphsage-node-classification.ipynb'>demo</a></td>
+    <td><a href='link-prediction/graphsage-link-prediction.ipynb'>demo</a></td>
     <td>
-      <span title='UnsupervisedSampler'><a href='embeddings/embeddings-unsupervised-graphsage-cora.ipynb'>US</a></span>
-      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-cora.ipynb'>DGI</a></span>
+      <span title='UnsupervisedSampler'><a href='embeddings/graphsage-unsupervised-sampler-embeddings.ipynb'>US</a></span>
+      <span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-embeddings.ipynb'>DGI</a></span>
     </td>
-    <td><a href='node-classification/graphsage/graphsage-pubmed-inductive-node-classification-example.ipynb'>demo</a></td>
+    <td><a href='node-classification/graphsage-inductive-node-classification.ipynb'>demo</a></td>
     <td></td>
   </tr>
   <tr>
@@ -192,21 +192,21 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td>yes</td>
     <td>yes</td>
-    <td><a href='link-prediction/hinsage/movielens-recommender.ipynb'>demo</a></td>
-    <td><span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-cora.ipynb'>DGI</a></span></td>
+    <td><a href='link-prediction/hinsage-link-prediction.ipynb'>demo</a></td>
+    <td><span title='DeepGraphInfomax'><a href='embeddings/deep-graph-infomax-embeddings.ipynb'>DGI</a></span></td>
     <td>yes</td>
     <td></td>
   </tr>
   <tr>
     <td>Node2Vec</td>
     <td></td>
     <td></td>
-    <td><a href='node-classification/node2vec/stellargraph-node2vec-weighted-random-walks.ipynb'>demo</a></td>
+    <td><a href='node-classification/node2vec-weighted-node-classification.ipynb'>demo</a></td>
     <td></td>
     <td></td>
-    <td><span title='via embedding vectors'><a href='node-classification/node2vec/stellargraph-node2vec-node-classification.ipynb'>via unsup.</a></span></td>
-    <td><span title='via embedding vectors'><a href='link-prediction/random-walks/cora-lp-demo.ipynb'>via unsup.</a></span></td>
-    <td><a href='embeddings/stellargraph-node2vec.ipynb'>demo</a></td>
+    <td><span title='via embedding vectors'><a href='node-classification/node2vec-node-classification.ipynb'>via unsup.</a></span></td>
+    <td><span title='via embedding vectors'><a href='link-prediction/node2vec-link-prediction.ipynb'>via unsup.</a></span></td>
+    <td><a href='embeddings/node2vec-embeddings.ipynb'>demo</a></td>
     <td></td>
     <td></td>
   </tr>
@@ -219,7 +219,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
-    <td><a href='embeddings/stellargraph-metapath2vec.ipynb'>demo</a></td>
+    <td><a href='embeddings/metapath2vec-embeddings.ipynb'>demo</a></td>
     <td></td>
     <td></td>
   </tr>
@@ -231,7 +231,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td>yes</td>
     <td></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
-    <td><span title='via embedding vectors'><a href='link-prediction/random-walks/ctdne-link-prediction.ipynb'>via unsup.</a></span></td>
+    <td><span title='via embedding vectors'><a href='link-prediction/ctdne-link-prediction.ipynb'>via unsup.</a></span></td>
     <td>yes</td>
     <td></td>
     <td></td>
@@ -243,9 +243,9 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td></td>
-    <td><span title='via embedding vectors'><a href='embeddings/watch-your-step-cora-demo.ipynb'>via unsup.</a></span></td>
+    <td><span title='via embedding vectors'><a href='embeddings/watch-your-step-embeddings.ipynb'>via unsup.</a></span></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
-    <td><a href='embeddings/watch-your-step-cora-demo.ipynb'>demo</a></td>
+    <td><a href='embeddings/watch-your-step-embeddings.ipynb'>demo</a></td>
     <td></td>
     <td></td>
   </tr>
@@ -257,7 +257,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
-    <td><a href='link-prediction/knowledge-graphs/complex.ipynb'>demo</a></td>
+    <td><a href='link-prediction/complex-link-prediction.ipynb'>demo</a></td>
     <td>yes</td>
     <td></td>
     <td></td>
@@ -270,7 +270,7 @@ These demos are displayed with detailed descriptions in the documentation: https
     <td></td>
     <td></td>
     <td><span title='via embedding vectors'>via unsup.</span></td>
-    <td><a href='link-prediction/knowledge-graphs/distmult.ipynb'>demo</a></td>
+    <td><a href='link-prediction/distmult-link-prediction.ipynb'>demo</a></td>
     <td>yes</td>
     <td></td>
     <td></td>

demos/basics/loading-networkx.ipynb

@@ -1038,7 +1038,7 @@
     "\n",
     "Revisit this document to use as a reminder, or [documentation](https://stellargraph.readthedocs.io/en/latest/api.html#stellargraph.StellarGraph.from_networkx) for the `StellarGraph.from_networkx` static method.\n",
     "\n",
-    "Once you've loaded your data, you can start doing machine learning: a good place to start is the [demo of the GCN algorithm on the Cora dataset for node classification](../node-classification/gcn/gcn-cora-node-classification-example.ipynb). Additionally, StellarGraph includes [many other demos of other algorithms, solving other tasks](../README.md)."
+    "Once you've loaded your data, you can start doing machine learning: a good place to start is the [demo of the GCN algorithm on the Cora dataset for node classification](../node-classification/gcn-node-classification.ipynb). Additionally, StellarGraph includes [many other demos of other algorithms, solving other tasks](../README.md)."
    ]
   },
   {

demos/basics/loading-pandas.ipynb

@@ -3307,7 +3307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This dataset, with this preparation, is used in [a demo of the GCN algorithm for node classification](../node-classification/gcn/gcn-cora-node-classification-example.ipynb). The task is to predict the subject of each node."
+    "This dataset, with this preparation, is used in [a demo of the GCN algorithm for node classification](../node-classification/gcn-node-classification.ipynb). The task is to predict the subject of each node."
    ]
   },
   {
@@ -3781,7 +3781,7 @@
     "\n",
     "Revisit this document to use as a reminder, or [the documentation](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.StellarGraph) for the `StellarGraph` class.\n",
     "\n",
-    "Once you've loaded your data, you can start doing machine learning: a good place to start is the [demo of the GCN algorithm on the Cora dataset for node classification](../node-classification/gcn/gcn-cora-node-classification-example.ipynb). Additionally, StellarGraph includes [many other demos of other algorithms, solving other tasks](../README.md)."
+    "Once you've loaded your data, you can start doing machine learning: a good place to start is the [demo of the GCN algorithm on the Cora dataset for node classification](../node-classification/gcn-node-classification.ipynb). Additionally, StellarGraph includes [many other demos of other algorithms, solving other tasks](../README.md)."
    ]
   },
   {

demos/basics/loading-saving-neo4j.ipynb

@@ -1813,7 +1813,7 @@
    "source": [
     "## Saving predictions into Neo4j\n",
     "\n",
-    "Most graph machine learning tasks will end up with some sort of predictions about some set of nodes or links in the graph. For example, [a node classification task]((../node-classification/gcn/gcn-cora-node-classification-example.ipynb) might result in either predicted scores for a node into different classes, or even just the single class that is the most likely. The formats of these are usually:\n",
+    "Most graph machine learning tasks will end up with some sort of predictions about some set of nodes or links in the graph. For example, [a node classification task]((../node-classification/gcn-node-classification.ipynb) might result in either predicted scores for a node into different classes, or even just the single class that is the most likely. The formats of these are usually:\n",
     "\n",
     "- scores: a multidimensional [NumPy](https://numpy.org) array. In the node classification example linked above, it's an array of floats of shape `(1, 2708, 7)`, where each of element along the axis of size 2708 represents a node, and the 7 numbers for that element represents the scores for each of the 7 classes for that node.\n",
     "- classes: a one-dimensional NumPy array. In the node classification example linked above, it's an array of strings of length 2708, where each element represents the predicted class for a node.\n",
@@ -1999,7 +1999,7 @@
     "\n",
     "Revisit this document to use as a reminder.\n",
     "\n",
-    "Once you've loaded your data, you can start doing machine learning: a good place to start is the [demo of the GCN algorithm on the Cora dataset for node classification](../node-classification/gcn/gcn-cora-node-classification-example.ipynb). Additionally, StellarGraph includes [many other demos of other algorithms, solving other tasks](../README.md). We also have experimental support for [running some algorithms directly using Neo4j](../connector/neo4j/README.md).\n",
+    "Once you've loaded your data, you can start doing machine learning: a good place to start is the [demo of the GCN algorithm on the Cora dataset for node classification](../node-classification/gcn-node-classification.ipynb). Additionally, StellarGraph includes [many other demos of other algorithms, solving other tasks](../README.md). We also have experimental support for [running some algorithms directly using Neo4j](../connector/neo4j/README.md).\n",
     "\n",
     "(We're still exploring the best ways to have StellarGraph work with Neo4j, so please [let us know](https://github.com/stellargraph/stellargraph#getting-help) your experience of using StellarGraph with Neo4j, both positive and negative.)"
    ]

demos/calibration/README.md

@@ -7,8 +7,8 @@ These demos are displayed with detailed descriptions in the documentation: https
 
 | Demo | Source |
 |---|---|
-| [Calibrating a GraphSAGE link prediction model](https://stellargraph.readthedocs.io/en/stable/demos/calibration/calibration-pubmed-link-prediction.html) | [source](calibration-pubmed-link-prediction.ipynb) |
-| [Calibrating a GraphSAGE node classification model](https://stellargraph.readthedocs.io/en/stable/demos/calibration/calibration-pubmed-node-classification.html) | [source](calibration-pubmed-node-classification.ipynb) |
+| [Calibrating a GraphSAGE link prediction model](https://stellargraph.readthedocs.io/en/stable/demos/calibration/calibration-link-prediction.html) | [source](calibration-link-prediction.ipynb) |
+| [Calibrating a GraphSAGE node classification model](https://stellargraph.readthedocs.io/en/stable/demos/calibration/calibration-node-classification.html) | [source](calibration-node-classification.ipynb) |
 
 The demo titles link to the latest, nicely rendered version. The 'source' links will open the demo in the application in which this README is being viewed, such as Jupyter Lab (ready for execution).
 <!-- DOCS LINKS -->