Release 0.11.0

StellarGraph is a Python library for machine learning on graphs and networks. It offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data.

Get started with StellarGraph's newest graph machine learning features with pip install stellargraph.

Major features and improvements

• The onboarding/getting-started process has been optimised and improved:
  • The README has been rewritten to highlight our numerous demos, and how to get help #1081
  • Example Jupyter notebooks can now be run directly in Google Colab and Binder, providing an easy way to get started with StellarGraph - simply click the and badges within each notebook. #1119.
  • The new demos/basics directory contains two notebooks demonstrating how to construct a StellarGraph object from Pandas, and from NetworkX #1074
  • The GCN node classification demo now has more explanation, to serve as an introduction to graph machine learning using StellarGraph #1125
• New algorithms:
  • Watch Your Step: computes node embeddings by simulating the effect of random walks, rather than doing them. #750.
  • Deep Graph Infomax: performs unsupervised node representation learning #978.
  • Temporal Random Walks (Continuous-Time Dynamic Network Embeddings): random walks that respect the time that each edge occurred (stored as edge weights) #1120.
  • ComplEx: computes multiplicative complex-number embeddings for entities and relationships (edge types) in knowledge graphs, which can be used for link prediction. #901 #1080
  • DistMult: computes multiplicative real-number embeddings for entities and relationships (edge types) in knowledge graphs, which can be used for link prediction. #755 #865 #1136

Breaking changes

• StellarGraph now requires TensorFlow 2.1 or greater, TensorFlow 2.0 is no longer supported #1008
• The legacy constructor using NetworkX graphs has been deprecated #1027. Migration: replace StellarGraph(some_networkx_graph) with StellarGraph.from_networkx(some_networkx_graph), and similarly for StellarDiGraph.
• The build method on model classes (such as GCN) has been renamed to in_out_tensors #1140. Migration: replace model.build() with model.in_out_tensors().
• The node_model and link_model methods on model classes has been replaced by in_out_tensors #1140 (see that PR for the exact list of types). Migration: replace model.node_model() with model.in_out_tensors() or model.in_out_tensors(multiplicity=1), and model.node_model() with model.in_out_tensors() or model.in_out_tensors(multiplicity=2).
• Re-exports of calibration and ensembling functionality from the top-level of the stellargraph module were deprecated, in favour of importing from the stellargraph.calibration or stellargraph.ensemble submodules directly #1107. Migration: replace uses of stellargraph.Ensemble with stellargraph.ensemble.Ensemble, and similarly for the other names (see #1107 for all replacements).
• StellarGraph.to_networkx parameters now use attr to refer to NetworkX attributes, not name or label #973. Migration: for any named parameters in graph.to_networkx(...), change node_type_name=... to node_type_attr=... and similarly edge_type_name to edge_type_attr, edge_weight_label to edge_weight_attr, feature_name to feature_attr.
• StellarGraph.nodes_of_type is deprecated in favour of the nodes method #1111. Migration: replace some_graph.nodes_of_type(some_type) with some_graph.nodes(node_type=some_type).
• StellarGraph.info parameters show_attributes and sample were deprecated #1110
• Some more layers and models had many parameters move from **kwargs to real arguments: Attri2Vec (#1128), ClusterGCN (#1129), GraphAttention & GAT (#1130), GraphSAGE & its aggregators (#1142), HinSAGE & its aggregators (#1143), RelationalGraphConvolution & RGCN (#1148). Invalid (e.g. incorrectly spelled) arguments would have been ignored previously, but now may fail with a TypeError; to fix, remove or correct the arguments.
• The method="chebyshev" option to FullBatchNodeGenerator, FullBatchLinkGenerator and GCN_Aadj_feats_op has been removed for now, because it needed significant revision to be correctly implemented #1028
• The fit_generator, evaluate_generator and predict_generator methods on Ensemble and BaggingEnsemble have been renamed to fit, evaluate and predict, to match the deprecation in TensorFlow 2.1 of the tensorflow.keras.Model methods of the same name #1065. Migration: remove the _generator suffix on these methods.
• The default_model method on Attri2Vec, GraphSAGE and HinSAGE has been deprecated, in favour of in_out_tensors #1145. Migration: replace model.default_model() with model.in_out_tensors().

Experimental features

Some new algorithms and features are still under active development, and are available as an experimental preview. However, they may not be easy to use: their documentation or testing may be incomplete, and they may change dramatically from release to release. The experimental status is noted in the documentation and at runtime via prominent warnings.

• GCNSupervisedGraphClassification: supervised graph classification model based on Graph Convolutional layers (GCN) #929.

Bug fixes and other changes

• StellarGraph.to_adjacency_matrix is at least 15× faster on undirected graphs #932
• ClusterNodeGenerator is now noticably faster, which makes training and predicting with a ClusterGCN model faster #1095. On a random graph with 1000 nodes and 5000 edges and 10 clusters, iterating over an epoch with q=1 (each clusters individually) is 2× faster, and is even faster for larger q. The model in the Cluster-GCN demo notebook using Cora trains 2× faster overall.
• The node_features=... parameter to StellarGraph.from_networkx now only needs to mention the node types that have features, when passing a dictionary of Pandas DataFrames. Node types that aren't mentioned will automatically have no features (zero-length feature vectors). #1082
• A subgraph method was added to StellarGraph for computing a node-induced subgraph #958
• A connected_components method was added to StellarGraph for computing the nodes involved in each connected component in a StellarGraph #958
• The info method on StellarGraph now shows only 20 node and edge types by default to be more useful for graphs with many types #993. This behaviour can be customized with the truncate=... parameter.
• The info method on StellarGraph now shows information about the size and type of each node type's feature vectors #979
• The EdgeSplitter class supports StellarGraph input (and will output StellarGraphs in this case), in addition to NetworkX graphs #1032
• The Attri2Vec model class stores its weights statefully, so they are shared between all tensors computed by build #1101
• The GCN model defaults for some parameters now match the GraphConvolution layer's defaults: specifically kernel_initializer (glorot_uniform) and bias_initializer (zeros) #1147
• The datasets submodule is now accessible as stellargraph.datasets, after just import stellargraph #1113
• All datasets in stellargraph.datasets now support a load method to create a StellarGraph object (and other information): AIFB (#982), CiteSeer (#989), Cora (#913), MovieLens (#947), PubMedDiabetes (#986). The demo notebooks using these datasets are now cleaner.
• Some new datasets were added to stellargraph.datasets:
  • MUTAG: a collection of graphs representing chemical compounds #960
  • WN18, WN18RR: knowledge graphs based on the WordNet linguistics data #977
  • FB15k, FB15k_237: knowledge graphs based on the FreeBase knowledge base #977
  • IAEnronEmployees: a small set of employees of Enron, and the many emails between them #1058
• Warnings now point to the call site of the function causing the warning, not the warnings.warn call inside StellarGraph; this means DeprecationWarnings will be visible in Jupyter notebooks and scripts run with Python 3.7 #1144
• Some code that triggered warnings from other libraries was fixed or removed #995 #1008, #1051, #1064, #1066
• Some demo notebooks have been updated or fixed: demos/use-cases/hateful-twitters.ipynb (#1019), rgcn-aifb-node-classification-example.ipynb (#983)
• The documentation "quick start" guide duplicated a lot of the information in the README, and so has been replaced with the latter #1096
• API documentation now lists items under their recommended import path, not their definition. For instance, stellargraph.StellarGraph instead of stellargraph.core.StellarGraph (#1127), stellargraph.layer.GCN instead of stellargraph.layer.gcn.GCN (#1150) and stellargraph.datasets.Cora instead of stellargraph.datasets.datasets.Cora (#1157)
• Some API documentation is now formatted better #1061, #1068, #1070, #1071
• DevOps changes:
  • Neo4j functionality is now tested on CI, and so will continue working #1046 #1050
  • CI: #967, #968, #1036, #1067, #1097
  • Other: #956, #962, #974