https://github.com/aveek-saha/graph-conv-net
A TensorFlow 2 implementation of Graph Convolutional Networks (GCN)
https://github.com/aveek-saha/graph-conv-net
gcn gcnn graph-convolutional-networks graph-neural-networks graphs tensoflow tensorflow2
Last synced: 8 months ago
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A TensorFlow 2 implementation of Graph Convolutional Networks (GCN)
- Host: GitHub
- URL: https://github.com/aveek-saha/graph-conv-net
- Owner: Aveek-Saha
- License: mit
- Created: 2020-08-22T15:31:54.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-06T14:06:11.000Z (almost 6 years ago)
- Last Synced: 2025-06-27T18:46:05.182Z (12 months ago)
- Topics: gcn, gcnn, graph-convolutional-networks, graph-neural-networks, graphs, tensoflow, tensorflow2
- Language: Python
- Homepage:
- Size: 174 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Graph Convolutional Networks
A TensorFlow 2 implementation of Graph Convolutional Networks for classification of nodes from the paper, Thomas N. Kipf, Max Welling, [Semi-Supervised Classification with Graph Convolutional Networks](http://arxiv.org/abs/1609.02907) (ICLR 2017)
This is my attempt at trying to understand and recreate the neural network from from the paper. You can find the official implementation here: https://github.com/tkipf/gcn
## Requirements
- tensorflow 2
- networkx
- numpy
- scikit-learn
## Run
To train and test the network with the CORA dataset.
```bash
python train.py
```
## Cite
Please cite the original 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}
}
```