pyg-team/pytorch_geometric 1.7.0

on GitHub

Major Features

• Temporal Graph Network and an example utilizing graph attention, (thanks to @emalgorithm)
• CorrectAndSmooth and an example on ogbn-products
• PyTorch Lightning support, see here for the accompanying examples (thanks to @tchaton)
• Sequential API, see here for the accompanying example
• FiLMConv and an example on PPI (thanks to @ldv1)
• SuperGAT and an example on Cora (thanks to @dongkwan-kim)
• MemPooling (thanks to @wsad1)
• PANConv (thanks to @YuGuangWang)
• DiffGroupNorm (thanks to @wsad1)
• ResGatedGraphConv (thanks to @ldv1)
• FAConv (thanks to @wsad1)
• AttentiveFP model for molecular graph learning and an example on ESOL (thanks to @thegodone)
• Shadow k-hop Sampler (currently requires torch-sparse from master)

Additional Features

• Inductive Deep Graph Infomax example (thanks to @harrygcoppock)
• WLConv and an example of the Weisfeiler-Lehman subtree kernel (thanks to @chrsmrrs)
• LabelPropagation
• AddTrainValTestMask transform for creating various splitting strategies (thanks to @dongkwan-kim)
• homophily measurement (thanks to @ldv1)
• to_cugraph conversion

Minor Changes

• More memory-efficient implementation of GCN2Conv
• Improved TransformerConv with the beta argument being input and message dependent (thanks to @ldv1)
• NeighborSampler now works with SparseTensor and supports an additional transform argument
• Batch.from_data_list now supports batching along a new dimension via returning None in Data.__cat_dim__, see here for the accompanying tutorial (thanks to @Linux-cpp-lisp)
• MetaLayer is now "jittable"
• Lazy loading of torch_geometric.nn and torch_geometric.datasets, leading to faster imports (thanks to @Linux-cpp-lisp)
• GNNExplainer now supports various output formats of the underlying GNN model (thanks to @wsad1)

Datasets

• JODIE datasets for temporal graph learning
• WordNet18RR (thanks to @minhtriet)
• Reddit2
• MixHopSyntheticDataset (thanks to @ldv1)
• NELL

Bugfixes

• Fixed SparseAdam usage in examples/metapath2vec.py (thanks to @declanmillar)
• Fixed from_networkx to support empty edge lists (thanks to @shakedbr)
• Fixed a numerical issue in softmax
• Fixed an issue in DenseGraphConv with aggr="max" (thanks to @quqixun)
• Fixed the norm computation in GraphSAINTSampler (thanks to @austintwang)
• Cartesian and LocalCartesian now compute Cartesian coordinates from target to source nodes (thanks to @ldv1)