Init commit

Author: cszhangzhen

.DS_Store

@@ -1 +1,34 @@
-# HGP-SL
+# HGP-SL
+Hierarchical Graph Pooling with Structure Learning (AAAI-2020).
+
+This is a PyTorch implementation of the HGP-SL algorithm, which learns a low-dimensional representation for the entire graph. Specifically, the graph pooling operation utilizes node features and graph structure information to perform down-sampling on graphs. Then, a structure learning layer is stacked on the pooling operation, which aims to learn a refined graph structure that can best preserve the essential topological information.
+
+
+## Requirements
+* python3
+* torch-scatter
+* torch-sparse
+* torch-cluster
+* torch-geometric
+
+Note:
+This code repository is heavily built on [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric), which is a Geometric Deep Learning Extension Library for PyTorch. Please refer [here](https://pytorch-geometric.readthedocs.io/en/latest/) for how to install and utilize the libary.
+
+
+### Run
+To run HGP-SL, just execute the following command for graph classification task:
+```
+python main.py
+```
+
+## Citing
+If you find HGP-SL useful for your research, please consider citing the following paper:
+```
+@inproceedings{zhang2020hierarch,
+  title={Hierarchical Graph Pooling with Structure Learning.},
+  author={Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang},
+  booktitle={Proceedings of the 34th AAAI Conference on Artificial Intelligence},
+  year={2020},
+  organization={AAAI}
+}
+```