[Roadmap] New PyTorch Geometric Releases · pyg-team/pytorch_geometric#1465

2020-07-27T06:12:50.000Z

# [Roadmap] ## 1.6.1 * [x] [**`WikiCSDataset`**](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.WikiCS) * [x] **`DeepGCN`** * [x] [`GENConv`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GENConv) * [x] [`DeepGCNLayer`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.models.DeepGCNLayer) * [x] [`ogn-proteins` example](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/ogbn_proteins_deepgcn.py) * [x] Fix `torch-sparse` reductions with `dim=0` * [x] Fix `torch-sparse` suppression of warnings * [x] **`PairNorm`** (https://github.com/rusty1s/pytorch_geometric/pull/1448) * [x] **`LayerNorm`** (https://github.com/rusty1s/pytorch_geometric/pull/1471) * [x] Fix `jittable` bug when installing PyG via `pip` * [x] PyTorch 1.6.0 support * [x] Test `GNNExplainer` with more models ## 1.6.2 * [x] Fix wrong URLs in datasets * [x] `WebKB` datasets from the [Geom-GCN paper](https://openreview.net/pdf?id=S1e2agrFvS) * [x] `GCN2Conv` from the [Simple and Deep Graph Convolutional Networks paper](https://arxiv.org/pdf/2007.02133.pdf) * [x] `TransformerConv` from [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://arxiv.org/pdf/2009.03509.pdf) * [x] Unsupervised GraphSAGE example (https://github.com/rusty1s/pytorch_geometric/pull/1514) [[Example](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/graph_sage_unsup.py)] * [x] **`DistributedDataParallel` support**: * [x] Large-scale node classification (https://github.com/rusty1s/pytorch_geometric/pull/1453) * [x] Batching of graphs (https://github.com/rusty1s/pytorch_geometric/issues/1417#issuecomment-664119294) [[Example](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/multi_gpu/distributed_batching.py)] * [x] `Node2Vec` implementation with `p != 1` and `q != 1` * Faster CPU implementations in `torch-scatter` and `torch-sparse` via **multi-threading**: * [x] `spmm` * [x] Add edge weights to the documentation of GNN formulations, see https://github.com/rusty1s/pytorch_geometric/issues/1449 * [x] Clean up documentation via `autosummary` (https://github.com/rusty1s/pytorch_geometric/pull/1791) * [x] `follow_batch` argument in `DataParallel` ## 1.6.3. * [x] `WikipediaNetwork` and `Actor` datasets from the [Geom-GCN paper](https://openreview.net/pdf?id=S1e2agrFvS) * [x] `torch_geometric.utils.homophily_ratio` ## 1.7.0+ * [x] Deep Graph Infomax Inductive Reddit Example (https://github.com/rusty1s/pytorch_geometric/pull/1859) (thanks to @harrygcoppock) * [x] **`TGN` (Temporal Graph Network)** (https://github.com/rusty1s/pytorch_geometric/pull/1519) (thanks to @emalgorithm) * [x] **Weisfeiler-Lehman kernel** (thanks to @chrsmrrs) * [x] **Label Propagation** * [x] **Correct&Smooth** * [x] **PyTorch Lightning** Example (GIN + GraphSAGE) * [x] **`FiLMConv`** * [x] Conda installation * [x] `cupgraph` conversions * [x] Fix `HyperGraphConv` https://github.com/rusty1s/pytorch_geometric/issues/1801 * [x] `scatter_mul` implementation * [ ] NARS * [ ] `spspmm` backward implementation * [ ] Multi-dimensional indexing of `SparseTensor`, *e.g.*, `adj[torch.tensor([0, 1, 2]), torch.tensor([0, 1, 2])]` * [ ] Allow edge types for any message passing GNN (https://github.com/rusty1s/pytorch_geometric/issues/1670) * [ ] Make all CPP sampling ops respect `torch.manual_seed` * [ ] `random_walk` with custom transition probabilities * [x] `torch.half` support * [ ] [Digraph Inception Convolutional Networks](https://proceedings.neurips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (https://github.com/rusty1s/pytorch_geometric/issues/1847) * Faster CPU implementations in `torch-scatter` and `torch-sparse` via **multi-threading**: * [x] `fps` * [ ] `spspmm` * [ ] `scatter` * [ ] `segment` * [ ] `NeighborSampler` with `GCNConv` example * [ ]`TrimNet` ([TrimNet: learning molecular representation from triplet messages for biomedicine](https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbaa266/5955940?redirectedFrom=fulltext)) * [x] Let `TransformerConv` be able to return attention weights (https://github.com/rusty1s/pytorch_geometric/pull/2807)

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Research direction: Identify a specific unchecked item from the roadmap (e.g., NARS, spspmm backward implementation) and investigate its implementation details and requirements.
Tech stack: pythonpytorch
Domain: machine learningai
Issue type: Research
Difficulty: 4
Estimated time: Over 1 week
Activity status: Active
Clarity: Mostly clear
Prerequisites: PythonPyTorchGit
Newbie friendliness: 10

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[Roadmap]

1.6.1

1.6.2

1.6.3.

1.7.0+

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