# gcn

**Repository Path**: wzxin/gcn

## Basic Information

- **Project Name**: gcn
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-08-03
- **Last Updated**: 2021-08-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Graph Convolutional Networks

This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of nodes in a graph, as described in our paper:
 
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)

## Installation

```bash
python setup.py install
```

## Requirements
* tensorflow (>0.12)
* networkx

## Run the demo

```bash
python train.py
```

## Data

In order to use your own data, you have to provide 
* an N by N adjacency matrix (N is the number of nodes), 
* an N by D feature matrix (D is the number of features per node), and
* an N by E binary label matrix (E is the number of classes).

Have a look at the `load_data()` function in `utils.py` for an example.

In this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://linqs.cs.umd.edu/projects/projects/lbc/. In our version (see `data` folder) we use dataset splits provided by https://github.com/kimiyoung/planetoid (Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov, [Revisiting Semi-Supervised Learning with Graph Embeddings](https://arxiv.org/abs/1603.08861), ICML 2016). 

You can specify a dataset as follows:

```bash
python train.py --dataset citeseer
```

(or by editing `train.py`)

## Models

You can choose between the following models: 
* `gcn`: Graph convolutional network (Thomas N. Kipf, Max Welling, [Semi-Supervised Classification with Graph Convolutional Networks](http://arxiv.org/abs/1609.02907), 2016)
* `gcn_cheby`: Chebyshev polynomial version of graph convolutional network as described in (Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375), NIPS 2016)
* `dense`: Basic multi-layer perceptron that supports sparse inputs


## 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}
}
```