1.6.2

Features

• GCN2Conv [Cora example, PPI example]
• TransformerConv
• New Dataset: WebKB
• New Google Colab: Explaining GNN Model Predictions using Captum (thanks to @m30m)
• Distributed training examples for node classification and graph classification (thanks to @maqy1995)
• Node2Vec can now handle different p and q values other than 1 (torch-cluster update required)
• GraphSAGE unsupervised training example (thanks to @yuanx749)
• Linear GAE example (thanks to @GuillaumeSalha)

Minor improvements

• The SIGN example now operates on mini-batches of nodes
• Improved data loading runtime of InMemoryDatasets
• NeighborSampler does now work with SparseTensor as input
• ToUndirected transform in order to convert directed graphs to undirected ones
• GNNExplainer does now allow for customizable edge and node feature loss reduction
• aggr can now passed to any GNN based on the MessagePassing interface (thanks to @m30m)
• Runtime improvements in SEAL (thanks to @muhanzhang)
• Runtime improvements in torch_geometric.utils.softmax (thanks to @Book1996)
• GAE.recon_loss now supports custom negative edge indices (thanks to @reshinthadithyan)
• Faster spmm computation and random_walk sampling on CPU (torch-sparse and torch-cluster updates required)
• DataParallel does now support the follow_batch argument
• Parallel approximate PPR computation in the GDC transform (thanks to @klicperajo)
• Improved documentation by providing an autosummary of all subpackages (thanks to @m30m)
• Improved documentation on how edge weights are handled in various GNNs (thanks to @m30m)

Bugfixes

• Fixed a bug in GATConv when computing attention coefficients in bipartite graphs
• Fixed a bug in GraphSAINTSampler that led to wrong edge feature sampling
• Fixed the DimeNet pretraining link
• Fixed a bug in processing ego-twitter and ego-gplus of the SNAPDataset collection
• Fixed a number of broken dataset URLs (ICEWS18, QM9, QM7b, MoleculeNet, Entities, PPI, Reddit, MNISTSuperpixels, ShapeNet)
• Fixed a bug in which MessagePassing.jittable() tried to write to a file without permission (thanks to @twoertwein)
• GCNConv does not require edge_weight in case normalize=False
• Batch.num_graphs will now report the correct amount of graphs in case of zero-sized graphs