2.0.4

PyG 2.0.4 🎉

A new minor PyG version release, bringing PyTorch 1.11 support to PyG. It further includes a variety of new features and bugfixes:

Features

• Added Quiver examples for multi-GU training using GraphSAGE (#4103), thanks to @eedalong and @luomai
• nn.model.to_captum: Full integration of explainability methods provided by the Captum library (#3990, #4076), thanks to @RBendias
• nn.conv.RGATConv: The relational graph attentional operator (#4031, #4110), thanks to @fork123aniket
• nn.pool.DMoNPooling: The spectral modularity pooling operator (#4166, #4242), thanks to @fork123aniket
• nn.*: Support for shape information in the documentation (#3739, #3889, #3893, #3946, #3981, #4009, #4120, #4158), thanks to @saiden89 and @arunppsg and @konstantinosKokos
• loader.TemporalDataLoader: A dataloader to load a TemporalData object in mini-batches (#3985, #3988), thanks to @otaviocx
• loader.ImbalancedSampler: A weighted random sampler that randomly samples elements according to class distribution (#4198)
• transforms.VirtualNode: A transform that adds a virtual node to a graph (#4163)
• transforms.LargestConnectedComponents: Selects the subgraph that corresponds to the largest connected components in the graph (#3949), thanks to @abojchevski
• utils.homophily: Support for class-insensitive edge homophily (#3977, #4152), thanks to @hash-ir and @jinjh0123
• utils.get_mesh_laplacian: Mesh Laplacian computation (#4187), thanks to @daniel-unyi-42

Datasets

• Added a dataset cheatsheet to the documentation that collects import graph statistics across a variety of datasets supported in PyG (#3807, #3817) (please consider helping us filling its remaining content)
• datasets.EllipticBitcoinDataset: A dataset of Bitcoin transactions (#3815), thanks to @shravankumar147

Minor Changes

• nn.models.MLP: MLPs can now either be initialized via a list of channels or by specifying hidden_channels and num_layers (#3957)
• nn.models.BasicGNN: Final Linear transformations are now always applied (except for jk=None) (#4042)
• nn.conv.MessagePassing: Message passing modules that make use of edge_updater are now jittable (#3765), thanks to @Padarn
• nn.conv.MessagePassing: (Official) support for min and mul aggregations (#4219)
• nn.LightGCN: Initialize embeddings via xavier_uniform for better model performance (#4083), thanks to @nishithshowri006
• nn.conv.ChebConv: Automatic eigenvalue approximation (#4106), thanks to @daniel-unyi-42
• nn.conv.APPNP: Added support for optional edge_weight, (690a01d), thanks to @YueeXiang
• nn.conv.GravNetConv: Support for torch.jit.script (#3885), thanks to @RobMcH
• nn.pool.global_*_pool: The batch vector is now optional (#4161)
• nn.to_hetero: Added a warning in case to_hetero is used on HeteroData metadata with unused destination node types (#3775)
• nn.to_hetero: Support for nested modules (ea135bf)
• nn.Sequential: Support for indexing (#3790)
• nn.Sequential: Support for OrderedDict as input (#4075)
• datasets.ZINC: Added an in-depth description of the task (#3832), thanks to @gasteigerjo
• datasets.FakeDataset: Support for different feature distributions across different labels (#4065), thanks to @arunppsg
• datasets.FakeDataset: Support for custom global attributes (#4074), thanks to @arunppsg
• transforms.NormalizeFeatures: Features will no longer be transformed in-place (ada5b9a)
• transforms.NormalizeFeatures: Support for negative feature values (6008e30)
• utils.is_undirected: Improved efficiency (#3789)
• utils.dropout_adj: Improved efficiency (#4059)
• utils.contains_isolated_nodes: Improved efficiency (970de13)
• utils.to_networkx: Support for to_undirected options (upper triangle vs. lower triangle) (#3901, #3948), thanks to @RemyLau
• graphgym: Support for custom metrics and loggers (#3494), thanks to @RemyLau
• graphgym.register: Register operations can now be used as class decorators (#3779, #3782)
• Documentation: Added a few exercises at the end of documentation tutorials (#3780), thanks to @PabloAMC
• Documentation: Added better installation instructions to CONTRIBUTUNG.md (#3803, #3991, #3995), thanks to @Cho-Geonwoo and @RBendias and @RodrigoVillatoro
• Refactor: Clean-up dependencies (#3908, #4133, #4172), thanks to @adelizer
• CI: Improved test runtimes (#4241)
• CI: Additional linting check via yamllint (#3886)
• CI: Additional linting check via isort (66b1780), thanks to @mananshah99
• torch.package: Model packaging via torch.package (#3997)

Bugfixes

• data.HeteroData: Fixed a bug in data.{attr_name}_dict in case data.{attr_name} does not exist (#3897)
• data.Data: Fixed data.is_edge_attr in case data.num_edges == 1 (#3880)
• data.Batch: Fixed a device mismatch bug in case a batch object was indexed that was created from GPU tensors (e6aa4c9, c549b3b)
• data.InMemoryDataset: Fixed a bug in which copy did not respect the underlying slice (d478dcb, #4223)
• nn.conv.MessagePassing: Fixed message passing with zero nodes/edges (#4222)
• nn.conv.MessagePassing: Fixed bipartite message passing with flow="target_to_source" (#3907)
• nn.conv.GeneralConv: Fixed an issue in case skip_linear=False and in_channels=out_channels (#3751), thanks to @danielegrattarola
• nn.to_hetero: Fixed model transformation in case node type names or edge type names contain whitespaces or dashes (#3882, b63a660)
• nn.dense.Linear: Fixed a bug in lazy initialization for PyTorch < 1.8.0 (973d17d, #4086)
• nn.norm.LayerNorm: Fixed a bug in the shape of weights and biases (#4030), thanks to @marshka
• nn.pool: Fixed torch.jit.script support for torch-cluster functions (#4047)
• datasets.TOSCA: Fixed a bug in which indices of faces started at 1 rather than 0 (8c282a0), thanks to @JRowbottomGit
• datasets.WikiCS: Fixed WikiCS to be undirected by default (#3796), thanks to @pmernyei
• Resolved inconsistency between utils.contains_isolated_nodes and data.has_isolated_nodes (#4138)
• graphgym: Fixed the loss function regarding multi-label classification (#4206), thanks to @RemyLau
• Documentation: Fixed typos, grammar and bugs (#3840, #3874, #3875, #4149), thanks to @itamblyn and @chrisyeh96 and @finquick