[Example] Add GraphMAE (#428)

examples/graphmae/.gitignore

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+data/
+.vscode

examples/graphmae/LICENSE

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

examples/graphmae/README.md

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