Fork Repository from gcn The code can be found in gcn/ folder

python3 setup.py install

• tensorflow (>0.12)
• networkx

python3 gcn/replicate_results.py

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, ICML 2016).

You can specify a dataset as follows:

python3 train.py --dataset citeseer

You can choose between the following models:

• gcn: Graph convolutional network (Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks, 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, NIPS 2016)
• dense: Basic multi-layer perceptron that supports sparse inputs

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}
}