# HGP-SL

**Repository Path**: milo7hao/HGP-SL

## Basic Information

- **Project Name**: HGP-SL
- **Description**: Hierarchical Graph Pooling with Structure Learning(AAAI-2020)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-10
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# HGP-SL
Hierarchical Graph Pooling with Structure Learning (AAAI-2020). Preprint version is available on [arXiv](https://arxiv.org/abs/1911.05954).

![](https://github.com/cszhangzhen/HGP-SL/blob/master/fig/model.png)

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
* pytorch
* 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.

### Datasets
Benchmarks are publicly available at [here](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets).

This folder contains the following comma separated text files (replace DS by the name of the dataset):

**n = total number of nodes**

**m = total number of edges**

**N = number of graphs**

**(1) DS_A.txt (m lines)** 

*sparse (block diagonal) adjacency matrix for all graphs, each line corresponds to (row, col) resp. (node_id, node_id)*

**(2) DS_graph_indicator.txt (n lines)**

*column vector of graph identifiers for all nodes of all graphs, the value in the i-th line is the graph_id of the node with node_id i*

**(3) DS_graph_labels.txt (N lines)** 

*class labels for all graphs in the dataset, the value in the i-th line is the class label of the graph with graph_id i*

**(4) DS_node_labels.txt (n lines)**

*column vector of node labels, the value in the i-th line corresponds to the node with node_id i*

There are OPTIONAL files if the respective information is available:

**(5) DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt)**

*labels for the edges in DS_A_sparse.txt* 

**(6) DS_edge_attributes.txt (m lines; same size as DS_A.txt)**

*attributes for the edges in DS_A.txt* 

**(7) DS_node_attributes.txt (n lines)** 

*matrix of node attributes, the comma seperated values in the i-th line is the attribute vector of the node with node_id i*

**(8) DS_graph_attributes.txt (N lines)** 

*regression values for all graphs in the dataset, the value in the i-th line is the attribute of the graph with graph_id i*


### 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}
}
```