https://github.com/tkipf/keras-gcn

Keras implementation of Graph Convolutional Networks
https://github.com/tkipf/keras-gcn

Last synced: 4 days ago
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Keras implementation of Graph Convolutional Networks

• Host: GitHub
• URL: https://github.com/tkipf/keras-gcn
• Owner: tkipf
• License: mit
• Created: 2017-01-27T09:20:28.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2021-04-19T23:36:21.000Z (over 3 years ago)
• Last Synced: 2024-11-02T23:32:51.374Z (14 days ago)
• Language: Python
• Homepage:
• Size: 171 KB
• Stars: 791
• Watchers: 23
• Forks: 266
• Open Issues: 29
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-gcn - tkipf/keras-gcn

README

        
Deep Learning on Graphs with Keras

====

Keras-based implementation of graph convolutional networks for semi-supervised classification.

Thomas N. Kipf, Max Welling, [Semi-Supervised Classification with Graph Convolutional Networks](http://arxiv.org/abs/1609.02907) (ICLR 2017)

For a high-level explanation, have a look at our blog post:

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

**NOTE: This code is not intended to reproduce the experiments from the paper as the initialization scheme, dropout scheme, and dataset splits differ from the original implementation in TensorFlow: https://github.com/tkipf/gcn**

Installation

------------

```python setup.py install```

Dependencies

-----

  * keras (1.0.9 or higher)

  * TensorFlow or Theano

Usage

-----

```python train.py```

Dataset reference (Cora)

----------

[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:

```

@inproceedings{kipf2017semi,

  title={Semi-Supervised Classification with Graph Convolutional Networks},

  author={Kipf, Thomas N. and Welling, Max},

  booktitle={International Conference on Learning Representations (ICLR)},

  year={2017}

}

```