GNN Pytorch Save
GNN方法和模型的Pytorch实现。Pytorch implementation of GNN.
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GNN-Pytorch
GNN方法和模型的Pytorch实现。Pytorch implementation of GNN.
这里更注重方法的学习，而不是获得更高的结果。
节点分类 - Node Classification
使用的数据集列表，有条件可以使用OGB数据集：
| Dataset | Nodes | Edges | Node Attr. | Classes | Train | Valid | Test |
|---|---|---|---|---|---|---|---|
| Cora | 2708 | 5429 | 1433 | 7 | 140 | 500 | 1000 |
| Cora-Full | 2708 | 5429 | 1433 | 7 | 1208 | 500 | 1000 |
| Citeseer | 3327 | 4732 | 3703 | 6 | 120 | 500 | 1000 |
| Citeseer-Full | 3327 | 4732 | 3703 | 6 | 1827 | 500 | 1000 |
| Pubmed | 19717 | 44338 | 500 | 3 | 60 | 500 | 1000 |
| Pubmed-Full | 19717 | 44338 | 500 | 3 | 18217 | 500 | 1000 |
各方法实验结果(Accuracy)列表：
| Status | Method | Paper | Cora | Citeseer | Pubmed |
|---|---|---|---|---|---|
| :heavy_check_mark: | GCN | Kipf and Welling, 2017 | 0.819 | 0.702 | 0.790 |
| :heavy_check_mark: | GraphSAGE | Hamilton and Ying et al., 2017 | 0.801 | 0.701 | 0.778 |
| :heavy_check_mark: | GAT | Velickovic et al., 2018 | 0.824 | 0.719 | 0.782 |
| :heavy_check_mark: | FastGCN* | Chen and Ma et al., 2018 | 0.854 | 0.779 | 0.855 |
| :heavy_check_mark: | GRAND | Feng and Zhang et al., 2020 | 0.839 | 0.726 | 0.797 |
* 使用Cora-Full，Pubmed-Full和Citeseer-Full数据集训练并评价。
图分类 - Graph Classification
使用的数据集列表，更多的数据集见TUDataset，有条件可以使用OGB数据集：
| Dataset | Graphs | Avg. Nodes | Avg. Edges | Node Attr. | Classes | Train | Valid | Test |
|---|---|---|---|---|---|---|---|---|
| DD | 1178 | 284.32 | 715.66 | 89 | 2 | 826 | 117 | 235 |
| NCI1 | 4110 | 29.87 | 32.30 | 37 | 2 | 2877 | 411 | 822 |
| PROTEINS | 1113 | 39.06 | 72.82 | 4 | 2 | 780 | 111 | 222 |
各方法实验结果(Accuracy)列表：
| Status | Method | Paper | DD | NCI1 | PROTEINS |
|---|---|---|---|---|---|
| DiffPool | Ying et al., 2018 | ||||
| Graph U-Nets | Gao et al., 2019 | ||||
| :heavy_check_mark: | SAGPoolg | Lee and Lee et al., 2019 | 0.723 | 0.763 | 0.757 |
| :heavy_check_mark: | SAGPoolh | Lee and Lee et al., 2019 | 0.745 | 0.648 | 0.743 |
| :heavy_check_mark: | MinCutPool | Bianchi and Grattarola et al., 2020 | 0.770 | 0.742 | 0.766 |
环境配置 - Packages
| 依赖 | 版本 | 安装 |
|---|---|---|
| python | 3.8.6 | conda create --name gnn python=3.8.6 |
| numpy | 1.20.0 | pip install numpy==1.20.0 |
| scipy | 1.6.0 | pip install scipy==1.6.0 |
| pyyaml | 5.4.1 | pip install pyyaml==5.4.1 |
| scikit-learn | 0.24.1 | pip install scikit-learn==0.24.1 |
| pytorch | 1.7.1 | conda install pytorch==1.7.1 cudatoolkit=11.0 -c pytorch |
| torch-geometric | 1.6.3 | Installation |
Open Source Agenda is not affiliated with "GNN Pytorch" Project. README Source: quqixun/GNN-Pytorch
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