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[Example] Add GraphMAE (#428)
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‎examples/graphmae/.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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build/
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develop-eggs/
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share/python-wheels/
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*.egg-info/
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*.egg
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MANIFEST
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# PyInstaller
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profile_default/
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ipython_config.py
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# pyenv
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# pipenv
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.vscode

‎examples/graphmae/LICENSE

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MIT License
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Copyright (c) 2022 Zhenyu Hou
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎examples/graphmae/README.md

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<p>
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<img src="imgs/fig.png" width="1000">
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<br />
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</p>
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<hr>
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<h1> GraphMAE: Self-Supervised Masked Graph Autoencoders </h1>
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CogDL implementation for KDD'22 paper: [GraphMAE: Self-Supervised Masked Graph Autoencoders](https://arxiv.org/abs/2205.10803).
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We also have a [Chinese blog](https://zhuanlan.zhihu.com/p/520389049) about GraphMAE on Zhihu (知乎), and an [English Blog](https://medium.com/p/7a641f8c66d0#4fae-bff62a5b8b4b) on Medium.
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GraphMAE is a generative self-supervised graph learning method, which achieves competitive or better performance than existing contrastive methods on tasks including *node classification*, *graph classification*, and *molecular property prediction*.
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<p>
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<img src="imgs/compare.png" width="520"><img src="imgs/ablation.jpg" width="270">
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<br />
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</p>
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<h2>Dependencies </h2>
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* Python >= 3.7
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* [Pytorch](https://pytorch.org/) >= 1.9.0
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* [cogdl](https://github.com/THUDM/cogdl) >= 0.5.3
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* pyyaml == 5.4.1
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<h2>Quick Start </h2>
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For quick start, you could run the scripts:
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**Node classification**
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```bash
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sh scripts/run_transductive.sh <dataset_name> <gpu_id> # for transductive node classification
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# example: sh scripts/run_transductive.sh cora/citeseer/pubmed/ogbn-arxiv 0
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sh scripts/run_inductive.sh <dataset_name> <gpu_id> # for inductive node classification
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# example: sh scripts/run_inductive.sh reddit/ppi 0
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# Or you could run the code manually:
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# for transductive node classification
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python main_transductive.py --dataset cora --encoder gat --decoder gat --seed 0 --device 0
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# for inductive node classification
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python main_inductive.py --dataset ppi --encoder gat --decoder gat --seed 0 --device 0
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```
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Supported datasets:
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* transductive node classification: `cora`, `citeseer`, `pubmed`, `ogbn-arxiv`
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* inductive node classification: `ppi`, `reddit`
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Run the scripts provided or add `--use_cfg` in command to reproduce the reported results.
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**Graph classification**
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```bash
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sh scripts/run_graph.sh <dataset_name> <gpu_id>
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# example: sh scripts/run_graph.sh mutag/imdb-b/imdb-m/proteins/... 0
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# Or you could run the code manually:
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python main_graph.py --dataset IMDB-BINARY --encoder gin --decoder gin --seed 0 --device 0
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```
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Supported datasets:
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- `IMDB-BINARY`, `IMDB-MULTI`, `PROTEINS`, `MUTAG`, `NCI1`, `REDDIT-BINERY`, `COLLAB`
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Run the scripts provided or add `--use_cfg` in command to reproduce the reported results.
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<h1> Citing </h1>
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If you find this work is helpful to your research, please consider citing our paper:
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```
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@inproceedings{hou2022graphmae,
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title={GraphMAE: Self-Supervised Masked Graph Autoencoders},
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author={Hou, Zhenyu and Liu, Xiao and Cen, Yukuo and Dong, Yuxiao and Yang, Hongxia and Wang, Chunjie and Tang, Jie},
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booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
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pages={594--604},
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year={2022}
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}
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```

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