CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file. The format is based on Keep a Changelog.

[2.3.0] - 2023-MM-DD

Added

• Add CPU affinity support for more data loaders (#6534)
• Added the BAMultiShapesDataset (#6541)
• Added the interfaces of a graph pooling framework (#6540)
• Added automatic n_id and e_id attributes to mini-batches produced by NodeLoader and LinkLoader (#6524)
• Added PGMExplainer to torch_geometric.contrib (#6149)
• Added a NumNeighbors helper class for specifying the number of neighbors when sampling (#6501, #6505)
• Added caching to is_node_attr() and is_edge_attr() calls (#6492)
• Added ToHeteroLinear and ToHeteroMessagePassing modules to accelerate to_hetero functionality (#5992, #6456)
• Added GraphMaskExplainer (#6284)
• Added the GRBCD and PRBCD adversarial attack models (#5972)
• Added dropout option to SetTransformer and GraphMultisetTransformer (#6484)
• Added option to customize loader arguments for evaluation in LightningNodeData and LightningLinkData (#6450, #6456)
• Added option to customize num_neighbors in NeighborSampler after instantiation (#6446)
• Added the Taobao dataset and a corresponding example for it (#6144)
• Added pyproject.toml (#6431)
• Added the torch_geometric.contrib sub-package (#6422)
• Warn on using latest documentation (#6418)
• Added basic pyright type checker support (#6415)
• Added a new external resource for link prediction (#6396)
• Added CaptumExplainer (#6383, #6387, #6433, #6487)
• Added support for custom HeteroData mini-batch class in remote backends (#6377)
• Added the GNNFF model (#5866)
• Added MLPAggregation, SetTransformerAggregation, GRUAggregation, and DeepSetsAggregation as adaptive readout functions (#6301, #6336, #6338)
• Added Dataset.to_datapipe for converting PyG datasets into a torchdata DataPipe(#6141)
• Added to_nested_tensor and from_nested_tensor functionality (#6329, #6330, #6331, #6332)
• Added the GPSConv Graph Transformer layer and example (#6326, #6327)
• Added networkit conversion utilities (#6321)
• Added global dataset attribute access via dataset.{attr_name} (#6319)
• Added the TransE KGE model and example (#6314)
• Added the Freebase FB15k_237 dataset (#3204)
• Added Data.update() and HeteroData.update() functionality (#6313)
• Added PGExplainer (#6204)
• Added the AirfRANS dataset (#6287)
• Added AttentionExplainer (#6279)
• Added (un)faithfulness explainability metric (#6090)
• Added fidelity explainability metric (#6116, #6510)
• Added subgraph visualization of GNN explanations (#6235, #6271)
• Added weighted negative sampling option in LinkNeighborLoader (#6264)
• Added the BA2MotifDataset explainer dataset (#6257)
• Added CycleMotif motif generator to generate n-node cycle shaped motifs (#6256)
• Added the InfectionDataset to evaluate explanations (#6222)
• Added characterization_score and fidelity_curve_auc explainer metrics (#6188)
• Added get_message_passing_embeddings (#6201)
• Added the PointGNNConv layer (#6194))
• Added GridGraph graph generator to generate grid graphs (#6220
• Added explainability metrics for when ground truth is available (#6137)
• Added visualize_feature_importance to support node feature visualizations (#6094)
• Added heterogeneous graph support to Explanation framework (#6091, #6218)
• Added a CustomMotif motif generator (#6179)
• Added ERGraph graph generator to generate Ergos-Renyi (ER) graphs (#6073
• Added BAGraph graph generator to generate Barabasi-Albert graphs - the usage of datasets.BAShapes is now deprecated (#6072
• Added explainability benchmark dataset framework (#6104)
• Added seed_time attribute to temporal NodeLoader outputs in case input_time is given (#6196)
• Added Data.edge_subgraph and HeteroData.edge_subgraph functionalities (#6193)
• Added input_time option to LightningNodeData and transform_sampler_output to NodeLoader and LinkLoader (#6187)
• Added summary for PyG/PyTorch models (#5859, #6161)
• Started adding torch.sparse support to PyG (#5906, #5944, #6003, #6033, #6514, #6532)
• Add inputs_channels back in training benchmark (#6154)
• Added support for dropping nodes in utils.to_dense_batch in case max_num_nodes is smaller than the number of nodes (#6124)
• Added the RandLA-Net architecture as an example (#5117)

Changed

• Improved code coverage (#6523, #6538)
• Fixed a bug in which data.to_heterogeneous() filtered attributs in the wrong dimension (#6522)
• Breaking Change: Temporal sampling will now also sample nodes with an equal timestamp to the seed time (requires pyg-lib>0.1.0) (#6517)
• Changed DataLoader workers with affinity to start at cpu0 (#6512)
• Allow 1D input to global_*_pool functions (#6504)
• Add information about dynamic shapes in RGCNConv (#6482)
• Fixed the use of types removed in numpy 1.24.0 (#6495)
• Fixed keyword parameters in examples/mnist_voxel_grid.py (#6478)
• Unified LightningNodeData and LightningLinkData code paths (#6473)
• Allow indices with any integer type in RGCNConv (#6463)
• Re-structured the documentation (#6420, #6423, #6429, #6440, #6443, #6445, #6452, #6453, #6458, #6459, #6460, #6490, #6491)
• Fix the default arguments of DataParallel class (#6376)
• Fix ImbalancedSampler on sliced InMemoryDataset (#6374)
• Breaking Change: Changed the interface and implementation of GraphMultisetTransformer (#6343)
• Fixed the approximate PPR variant in transforms.GDC to not crash on graphs with isolated nodes (#6242)
• Added a warning when accesing InMemoryDataset.data (#6318)
• Drop SparseTensor dependency in GraphStore (#5517)
• Replace NeighborSampler with NeighborLoader in the distributed sampling example (#6204)
• Fixed the filtering of node features in transforms.RemoveIsolatedNodes (#6308)
• Fixed a bug in DimeNet that causes a output dimension mismatch (#6305)
• Fixed Data.to_heterogeneous() with empty edge_index (#6304)
• Unify Explanation.node_mask and Explanation.node_feat_mask (#6267)
• Moved thresholding config of the Explainer to Explanation (#6215)
• Fixed a bug in the output order in HeteroLinear for un-sorted type vectors (#6198)
• Breaking Change: Move ExplainerConfig arguments to the Explainer class (#6176)
• Refactored NeighborSampler to be input-type agnostic (#6173)
• Infer correct CUDA device ID in profileit decorator (#6164)
• Correctly use edge weights in GDC example (#6159)
• Breaking Change: Moved PyTorch Lightning data modules to torch_geometric.data.lightning (#6140)
• Make torch_sparse an optional dependency (#6132, #6134, #6138, #6139)
• Optimized utils.softmax implementation (#6113, #6155)
• Optimized topk implementation for large enough graphs (#6123)

Removed

• Removed most of the torch-scatter dependencies (#6394, #6395, #6399, #6400)
• Removed the deprecated classes GNNExplainer and Explainer from nn.models (#6382)
• Removed target_index argument in the Explainer interface (#6270)
• Removed Aggregation.set_validate_args option (#6175)

[2.2.0] - 2022-12-01

Added

• Extended GNNExplainer to support edge level explanations (#6056, #6083)
• Added CPU affinitization for NodeLoader (#6005)
• Added triplet sampling in LinkNeighborLoader (#6004)
• Added FusedAggregation of simple scatter reductions (#6036)
• Added a to_smiles function (#6038)
• Added option to make normalization coefficients trainable in PNAConv (#6039)
• Added semi_grad option in VarAggregation and StdAggregation (#6042)
• Allow for fused aggregations in MultiAggregation (#6036, #6040)
• Added HeteroData support for to_captum_model and added to_captum_input (#5934)
• Added HeteroData support in RandomNodeLoader (#6007)
• Added bipartite GraphSAGE example (#5834)
• Added LRGBDataset to include 5 datasets from the Long Range Graph Benchmark (#5935)
• Added a warning for invalid node and edge type names in HeteroData (#5990)
• Added PyTorch 1.13 support (#5975)
• Added int32 support in NeighborLoader (#5948)
• Add dgNN support and FusedGATConv implementation (#5140)
• Added lr_scheduler_solver and customized lr_scheduler classes (#5942)
• Add to_fixed_size graph transformer (#5939)
• Add support for symbolic tracing of SchNet model (#5938)
• Add support for customizable interaction graph in SchNet model (#5919)
• Started adding torch.sparse support to PyG (#5906, #5944, #6003)
• Added HydroNet water cluster dataset (#5537, #5902, #5903)
• Added explainability support for heterogeneous GNNs (#5886)
• Added SparseTensor support to SuperGATConv (#5888)
• Added TorchScript support for AttentiveFP (#5868)
• Added num_steps argument to training and inference benchmarks (#5898)
• Added torch.onnx.export support (#5877, #5997)
• Enable VTune ITT in inference and training benchmarks (#5830, #5878)
• Add training benchmark (#5774)
• Added a "Link Prediction on MovieLens" Colab notebook (#5823)
• Added custom sampler support in LightningDataModule (#5820)
• Added a return_semantic_attention_weights argument HANConv (#5787)
• Added disjoint argument to NeighborLoader and LinkNeighborLoader (#5775)
• Added support for input_time in NeighborLoader (#5763)
• Added disjoint mode for temporal LinkNeighborLoader (#5717)
• Added HeteroData support for transforms.Constant (#5700)
• Added np.memmap support in NeighborLoader (#5696)
• Added assortativity that computes degree assortativity coefficient (#5587)
• Added SSGConv layer (#5599)
• Added shuffle_node, mask_feature and add_random_edge augmentation methdos (#5548)
• Added dropout_path augmentation that drops edges from a graph based on random walks (#5531)
• Add support for filling labels with dummy values in HeteroData.to_homogeneous() (#5540)
• Added temporal_strategy option to neighbor_sample (#5576)
• Added torch_geometric.sampler package to docs (#5563)
• Added the DGraphFin dynamic graph dataset (#5504)
• Added dropout_edge augmentation that randomly drops edges from a graph - the usage of dropout_adj is now deprecated (#5495)
• Added dropout_node augmentation that randomly drops nodes from a graph (#5481)
• Added AddRandomMetaPaths that adds edges based on random walks along a metapath (#5397)
• Added WLConvContinuous for performing WL refinement with continuous attributes (#5316)
• Added print_summary method for the torch_geometric.data.Dataset interface (#5438)
• Added sampler support to LightningDataModule (#5456, #5457)
• Added official splits to MalNetTiny dataset (#5078)
• Added IndexToMask and MaskToIndex transforms (#5375, #5455)
• Added FeaturePropagation transform (#5387)
• Added PositionalEncoding (#5381)
• Consolidated sampler routines behind torch_geometric.sampler, enabling ease of extensibility in the future (#5312, #5365, #5402, #5404), #5418)
• Added pyg-lib neighbor sampling (#5384, #5388)
• Added pyg_lib.segment_matmul integration within HeteroLinear (#5330, #5347))
• Enabled bf16 support in benchmark scripts (#5293, #5341)
• Added Aggregation.set_validate_args option to skip validation of dim_size (#5290)
• Added SparseTensor support to inference and training benchmark suite (#5242, #5258, #5881)
• Added experimental mode in inference benchmarks (#5254)
• Added node classification example instrumented with Weights and Biases (W&B) logging and W&B Sweeps (#5192)
• Added experimental mode for utils.scatter (#5232, #5241, #5386)
• Added missing test labels in HGBDataset (#5233)
• Added BaseStorage.get() functionality (#5240)
• Added a test to confirm that to_hetero works with SparseTensor (#5222)
• Added torch_geometric.explain module with base functionality for explainability methods (#5804, #6054, #6089)

Changed

• Moved and adapted GNNExplainer from torch_geometric.nn to torch_geometric.explain.algorithm (#5967, #6065)
• Optimized scatter implementations for CPU/GPU, both with and without backward computation (#6051, #6052)
• Support temperature value in dense_mincut_pool (#5908)
• Fixed a bug in which VirtualNode mistakenly treated node features as edge features (#5819)
• Fixed setter and getter handling in BaseStorage (#5815)
• Fixed path in hetero_conv_dblp.py example (#5686)
• Fix auto_select_device routine in GraphGym for PyTorch Lightning>=1.7 (#5677)
• Support in_channels with tuple in GENConv for bipartite message passing (#5627, #5641)
• Handle cases of not having enough possible negative edges in RandomLinkSplit (#5642)
• Fix RGCN+pyg-lib for LongTensor input (#5610)
• Improved type hint support (#5842, #5603, #5659, #5664, #5665, #5666, #5667, #5668, #5669, #5673, #5675, #5673, #5678, #5682, #5683, #5684, #5685, #5687, #5688, #5695, #5699, #5701, #5702, #5703, #5706, #5707, #5710, #5714, #5715, #5716, #5722, #5724, #5725, #5726, #5729, #5730, #5731, #5732, #5733, #5743, #5734, #5735, #5736, #5737, #5738, #5747, #5752, #5753, #5754, #5756, #5757, #5758, #5760, #5766, #5767, #5768), #5781, #5778, #5797, #5798, #5799, #5800, #5806, #5810, #5811, #5828, #5847, #5851, #5852)
• Avoid modifying mode_kwargs in MultiAggregation (#5601)
• Changed BatchNorm to allow for batches of size one during training (#5530, #5614)
• Integrated better temporal sampling support by requiring that local neighborhoods are sorted according to time (#5516, #5602)
• Fixed a bug when applying several scalers with PNAConv (#5514)
• Allow . in ParameterDict key names (#5494)
• Renamed drop_unconnected_nodes to drop_unconnected_node_types and drop_orig_edges to drop_orig_edge_types in AddMetapaths (#5490)
• Improved utils.scatter performance by explicitly choosing better implementation for add and mean reduction (#5399)
• Fix to_dense_adj with empty edge_index (#5476)
• The AttentionalAggregation module can now be applied to compute attentin on a per-feature level (#5449)
• Ensure equal lenghts of num_neighbors across edge types in NeighborLoader (#5444)
• Fixed a bug in TUDataset in which node features were wrongly constructed whenever node_attributes only hold a single feature (e.g., in PROTEINS) (#5441)
• Breaking change: removed num_neighbors as an attribute of loader (#5404)
• ASAPooling is now jittable (#5395)
• Updated unsupervised GraphSAGE example to leverage LinkNeighborLoader (#5317)
• Replace in-place operations with out-of-place ones to align with torch.scatter_reduce API (#5353)
• Breaking bugfix: PointTransformerConv now correctly uses sum aggregation (#5332)
• Improve out-of-bounds error message in MessagePassing (#5339)
• Allow file names of a Dataset to be specified as either property and method (#5338)
• Fixed separating a list of SparseTensor within InMemoryDataset (#5299)
• Improved name resolving of normalization layers (#5277)
• Fail gracefully on GLIBC errors within torch-spline-conv (#5276)
• Fixed Dataset.num_classes in case a transform modifies data.y (#5274)
• Allow customization of the activation function within PNAConv (#5262)
• Do not fill InMemoryDataset cache on dataset.num_features (#5264)
• Changed tests relying on dblp datasets to instead use synthetic data (#5250)
• Fixed a bug for the initialization of activation function examples in custom_graphgym (#5243)
• Allow any integer tensors when checking edge_index input to message passing (5281)

Removed

• Removed scatter_reduce option from experimental mode (#5399)

[2.1.0] - 2022-08-17

Added

• Added the test for DeepGCNLayer (#5704)
• Allow . in ModuleDict key names (#5227)
• Added edge_label_time argument to LinkNeighborLoader (#5137, #5173)
• Let ImbalancedSampler accept torch.Tensor as input (#5138)
• Added flow argument to gcn_norm to correctly normalize the adjacency matrix in GCNConv (#5149)
• NeighborSampler supports graphs without edges (#5072)
• Added the MeanSubtractionNorm layer (#5068)
• Added pyg_lib.segment_matmul integration within RGCNConv (#5052, #5096)
• Support SparseTensor as edge label in LightGCN (#5046)
• Added support for BasicGNN models within to_hetero (#5091)
• Added support for computing weighted metapaths in AddMetapaths (#5049)
• Added inference benchmark suite (#4915)
• Added a dynamically sized batch sampler for filling a mini-batch with a variable number of samples up to a maximum size (#4972)
• Added fine grained options for setting bias and dropout per layer in the MLP model (#4981)
• Added EdgeCNN model (#4991)
• Added scalable inference mode in BasicGNN with layer-wise neighbor loading (#4977)
• Added inference benchmarks (#4892, #5107)
• Added PyTorch 1.12 support (#4975)
• Added unbatch_edge_index functionality for splitting an edge_index tensor according to a batch vector (#4903)
• Added node-wise normalization mode in LayerNorm (#4944)
• Added support for normalization_resolver (#4926, #4951, #4958, #4959)
• Added notebook tutorial for torch_geometric.nn.aggr package to documentation (#4927)
• Added support for follow_batch for lists or dictionaries of tensors (#4837)
• Added Data.validate() and HeteroData.validate() functionality (#4885)
• Added LinkNeighborLoader support to LightningDataModule (#4868)
• Added predict() support to the LightningNodeData module (#4884)
• Added time_attr argument to LinkNeighborLoader (#4877, #4908)
• Added a filter_per_worker argument to data loaders to allow filtering of data within sub-processes (#4873)
• Added a NeighborLoader benchmark script (#4815, #4862)
• Added support for FeatureStore and GraphStore in NeighborLoader (#4817, #4851, #4854, #4856, #4857, #4882, #4883, #4929, #4992, #4962, #4968, #5037, #5088, #5270, #5307, #5318)
• Added a normalize parameter to dense_diff_pool (#4847)
• Added size=None explanation to jittable MessagePassing modules in the documentation (#4850)
• Added documentation to the DataLoaderIterator class (#4838)
• Added GraphStore support to Data and HeteroData (#4816)
• Added FeatureStore support to Data and HeteroData (#4807, #4853)
• Added FeatureStore and GraphStore abstractions (#4534, #4568, #5120)
• Added support for dense aggregations in global_*_pool (#4827)
• Added Python version requirement (#4825)
• Added TorchScript support to JumpingKnowledge module (#4805)
• Added a max_sample argument to AddMetaPaths in order to tackle very dense metapath edges (#4750)
• Test HANConv with empty tensors (#4756, #4841)
• Added the bias vector to the GCN model definition in the "Create Message Passing Networks" tutorial (#4755)
• Added transforms.RootedSubgraph interface with two implementations: RootedEgoNets and RootedRWSubgraph (#3926)
• Added ptr vectors for follow_batch attributes within Batch.from_data_list (#4723)
• Added torch_geometric.nn.aggr package (#4687, #4721, #4731, #4762, #4749, #4779, #4863, #4864, #4865, #4866, #4872, #4934, #4935, #4957, #4973, #4973, #4986, #4995, #5000, #5034, #5036, #5039, #4522, #5033, #5085, #5097, #5099, #5104, #5113, #5130, #5098, #5191)
• Added the DimeNet++ model (#4432, #4699, #4700, #4800)
• Added an example of using PyG with PyTorch Ignite (#4487)
• Added GroupAddRev module with support for reducing training GPU memory (#4671, #4701, #4715, #4730)
• Added benchmarks via wandb (#4656, #4672, #4676)
• Added unbatch functionality (#4628)
• Confirm that to_hetero() works with custom functions, e.g., dropout_adj (4653)
• Added the MLP.plain_last=False option (4652)
• Added a check in HeteroConv and to_hetero() to ensure that MessagePassing.add_self_loops is disabled (4647)
• Added HeteroData.subgraph(), HeteroData.node_type_subgraph() and HeteroData.edge_type_subgraph() support (#4635)
• Added the AQSOL dataset (#4626)
• Added HeteroData.node_items() and HeteroData.edge_items() functionality (#4644)
• Added PyTorch Lightning support in GraphGym (#4511, #4516 #4531, #4689, #4843)
• Added support for returning embeddings in MLP models (#4625)
• Added faster initialization of NeighborLoader in case edge indices are already sorted (via is_sorted=True) (#4620, #4702)
• Added AddPositionalEncoding transform (#4521)
• Added HeteroData.is_undirected() support (#4604)
• Added the Genius and Wiki datasets to nn.datasets.LINKXDataset (#4570, #4600)
• Added nn.aggr.EquilibrumAggregation implicit global layer (#4522)
• Added support for graph-level outputs in to_hetero (#4582)
• Added CHANGELOG.md (#4581)
• Added HeteroData support to the RemoveIsolatedNodes transform (#4479)
• Added HeteroData.num_features functionality (#4504)
• Added support for projecting features before propagation in SAGEConv (#4437)
• Added Geom-GCN splits to the Planetoid datasets (#4442)
• Added a LinkNeighborLoader for training scalable link predictions models #4396, #4439, #4441, #4446, #4508, #4509)
• Added an unsupervised GraphSAGE example on PPI (#4416)
• Added support for LSTM aggregation in SAGEConv (#4379)
• Added support for floating-point labels in RandomLinkSplit (#4311, #4383)
• Added support for torch.data DataPipes (#4302, #4345, #4349)
• Added support for the cosine argument in the KNNGraph/RadiusGraph transforms (#4344)
• Added support graph-level attributes in networkx conversion (#4343)
• Added support for renaming node types via HeteroData.rename (#4329)
• Added an example to load a trained PyG model in C++ (#4307)
• Added a MessagePassing.explain_message method to customize making explanations on messages (#4278, #4448))
• Added support for GATv2Conv in the nn.models.GAT model (#4357)
• Added HeteroData.subgraph functionality (#4243)
• Added the MaskLabel module and a corresponding masked label propagation example (#4197)
• Added temporal sampling support to NeighborLoader (#4025)
• Added an example for unsupervised heterogeneous graph learning based on "Deep Multiplex Graph Infomax" (#3189)

Changed

• Changed docstring for RandomLinkSplit (#5190)
• Switched to PyTorch scatter_reduce implementation - experimental feature (#5120)
• Fixed RGATConv device mismatches for f-scaled mode (#5187]
• Allow for multi-dimensional edge_labels in LinkNeighborLoader (#5186]
• Fixed GINEConv bug with non-sequential input (#5154]
• Improved error message (#5095)
• Fixed HGTLoader bug which produced outputs with missing edge types (#5067)
• Fixed dynamic inheritance issue in data batching (#5051)
• Fixed load_state_dict in Linear with strict=False mode (5094)
• Fixed typo in MaskLabel.ratio_mask (5093)
• Fixed data.num_node_features computation for sparse matrices (5089)
• Fixed torch.fx bug with torch.nn.aggr package (#5021))
• Fixed GenConv test (4993)
• Fixed packaging tests for Python 3.10 (4982)
• Changed act_dict (part of graphgym) to create individual instances instead of reusing the same ones everywhere (4978)
• Fixed issue where one-hot tensors were passed to F.one_hot (4970)
• Fixed bool arugments in argparse in benchmark/ (#4967)
• Fixed BasicGNN for num_layers=1, which now respects a desired number of out_channels (#4943)
• len(batch) will now return the number of graphs inside the batch, not the number of attributes (#4931)
• Fixed data.subgraph generation for 0-dim tensors (#4932)
• Removed unnecssary inclusion of self-loops when sampling negative edges (#4880)
• Fixed InMemoryDataset inferring wrong len for lists of tensors (#4837)
• Fixed Batch.separate when using it for lists of tensors (#4837)
• Correct docstring for SAGEConv (#4852)
• Fixed a bug in TUDataset where pre_filter was not applied whenever pre_transform was present
• Renamed RandomTranslate to RandomJitter - the usage of RandomTranslate is now deprecated (#4828)
• Do not allow accessing edge types in HeteroData with two node types when there exists multiple relations between these types (#4782)
• Allow edge_type == rev_edge_type argument in RandomLinkSplit (#4757, #5221)
• Fixed a numerical instability in the GeneralConv and neighbor_sample tests (#4754)
• Fixed a bug in HANConv in which destination node features rather than source node features were propagated (#4753)
• Fixed versions of checkout and setup-python in CI (#4751)
• Fixed protobuf version (#4719)
• Fixed the ranking protocol bug in the RGCN link prediction example (#4688)
• Math support in Markdown (#4683)
• Allow for setter properties in Data (#4682, #4686)
• Allow for optional edge_weight in GCN2Conv (#4670)
• Fixed the interplay between TUDataset and pre_transform that modify node features (#4669)
• Make use of the pyg_sphinx_theme documentation template (#4664, #4667)
• Refactored reading molecular positions from sdf file for qm9 datasets (4654)
• Fixed MLP.jittable() bug in case return_emb=True (#4645, #4648)
• The generated node features of StochasticBlockModelDataset are now ordered with respect to their labels (#4617)
• Fixed typos in the documentation (#4616, #4824, #4895, #5161)
• The bias argument in TAGConv is now actually applied (#4597)
• Fixed subclass behaviour of process and download in Datsaet (#4586)
• Fixed filtering of attributes for loaders in case __cat_dim__ != 0 (#4629)
• Fixed SparseTensor support in NeighborLoader (#4320)
• Fixed average degree handling in PNAConv (#4312)
• Fixed a bug in from_networkx in case some attributes are PyTorch tensors (#4486)
• Added a missing clamp in DimeNet (#4506, #4562)
• Fixed the download link in DBP15K (#4428)
• Fixed an autograd bug in DimeNet when resetting parameters (#4424)
• Fixed bipartite message passing in case flow="target_to_source" (#4418)
• Fixed a bug in which num_nodes was not properly updated in the FixedPoints transform (#4394)
• PyTorch Lightning >= 1.6 support (#4377)
• Fixed a bug in which GATConv was not jittable (#4347)
• Fixed a bug in which the GraphGym config was not stored in each specific experiment directory (#4338)
• Fixed a bug in which nn.models.GAT did not produce out_channels-many output channels (#4299)
• Fixed mini-batching with empty lists as attributes (#4293)
• Fixed a bug in which GCNConv could not be combined with to_hetero on heterogeneous graphs with one node type (#4279)

Removed

• Remove internal metrics in favor of torchmetrics (#4287)