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pygcn

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Graph Convolutional Networks in PyTorch

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- - cora.cites
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  - README
- __init__.py
- layers.py
- models.py
- train.py
- utils.py
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figure.png
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README.md
setup.py

# Installation Guide

1. Get the code

git clone https://github.com/tkipf/pygcn

Downloads the entire project code from GitHub to your computer.

cd pygcn

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install .

Installs the package published on PyPI directly — no need to clone the source.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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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](https://raw.githubusercontent.com/tkipf/pygcn/master/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}
}
```