https://github.com/danesherbs/gcn

Implementation of Graph Convolutional Networks in TensorFlow

https://github.com/danesherbs/gcn

Repository

Implementation of Graph Convolutional Networks in TensorFlow

https://github.com/danesherbs/gcn/blob/master/

# 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 (Michal 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

## Graph classification

Our framework also supports batch-wise classification of multiple graph instances (of potentially different size) with an adjacency matrix each. It is best to concatenate respective feature matrices and build a (sparse) block-diagonal matrix where each block corresponds to the adjacency matrix of one graph instance. For pooling (in case of graph-level outputs as opposed to node-level outputs) it is best to specify a simple pooling matrix that collects features from their respective graph instances, as illustrated below:

![graph_classification](https://user-images.githubusercontent.com/7347296/34198790-eb5bec96-e56b-11e7-90d5-157800e042de.png)


## 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}
}
```

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