# Pytorch-GCN

**Repository Path**: wandehua/Pytorch-GCN

## Basic Information

- **Project Name**: Pytorch-GCN
- **Description**: 图卷积网络GCN的Pytorch实现
源码:：https://github.com/tkipf/gcn
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 3
- **Created**: 2020-07-28
- **Last Updated**: 2022-09-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Graph Convolutional Networks in PyTorch
====

PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1].

For a high-level introduction to GCNs, see:

Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016)

![Graph Convolutional Networks](figure.png)

Note: There are subtle differences between the TensorFlow implementation in https://github.com/tkipf/gcn and this PyTorch re-implementation. This re-implementation serves as a proof of concept and is not intended for reproduction of the results reported in [1].

This implementation makes use of the Cora dataset from [2].

## Installation

```python setup.py install```

## Requirements

  * PyTorch 0.4 or 0.5
  * Python 2.7 or 3.6

## Usage

```python train.py```

## References

[1] [Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016](https://arxiv.org/abs/1609.02907)

[2] [Sen et al., Collective Classification in Network Data, AI Magazine 2008](http://linqs.cs.umd.edu/projects/projects/lbc/)

## Cite

Please cite our paper if you use this code in your own work:

```
@article{kipf2016semi,
  title={Semi-Supervised Classification with Graph Convolutional Networks},
  author={Kipf, Thomas N and Welling, Max},
  journal={arXiv preprint arXiv:1609.02907},
  year={2016}
}
```