pyg-team/pytorch_geometric 1.5.0 on GitHub

This release is a big one thanks to many wonderful contributors. You guys are awesome!

Breaking Changes and Highlights

NeighborSampler got completely revamped: it's now much faster, allows for parallel sampling, and allows to easily apply skip-connections or self-loops. See examples/reddit.py or the newly introduced OGB examples (examples/ogbn_products_sage.py and examples/ogbn_products_gat.py). The latter also sets a new SOTA on the OGB leaderboards (reaching 0.7945 ± 0.0059 test accuracy)
SAGEConv now uses concat=True by default, and there is no option to disable it anymore
Node2Vec got enhanced by a parallel sampling mechanism, and as a result, its API slightly changed
MetaPath2Vec: The first model in PyG that is able to operate on heteregenous graphs
GNNExplainer: Generating explanations for graph neural networks
GraphSAINT: A graph sampling based inductive learning method
SchNet model for learning on molecular graphs, comes with pre-trained weights for each target of the QM9 dataset (thanks to @Nyuten)

ASAPooling: Adaptive structure aware pooling for learning hierarchical graph representations (thanks to @ekagra-ranjan)
ARGVA node clustering example, see examples/argva_node_clustering.py (thanks to @gsoosk)
MFConv: Molecular fingerprint graph convolution operator (thanks to @rhsimplex)
GIN-E-Conv that extends the GINConv to also account for edge features
DimeNet: Directional message passing for molecular graphs
SIGN: Scalable inception graph neural networks
GravNetConv (thanks to @jkiesele)

GATConv can now return attention weights via the return_attention_weights argument (thanks to @douglasrizzo)
InMemoryDataset now has a copy method that converts sliced datasets back into a contiguous memory layout
Planetoid got enhanced by the ability to let users choose between different splitting methods (thanks to @dongkwan-kim)
k_hop_subgraph: Computes the k-hop subgraph around a subset of nodes
geodesic_distance: Geodesic distances can now be computed in parallel (thanks to @jannessm)
tree_decomposition: The tree decompostion algorithm for generating junction trees from molecules
SortPool benchmark script now uses 1-D convolutions after pooling, leading to better performance (thanks to @muhanzhang)