Implementation of Graph Auto-Encoders in TensorFlow
This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper:
T. N. Kipf, M. Welling, Variational Graph Auto-Encoders, NIPS Workshop on Bayesian Deep Learning (2016)
Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs.
GAEs have successfully been used for:
GAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs:
T. N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR (2017).
A high-level introduction is given in our blog post:
Thomas Kipf, Graph Convolutional Networks (2016)
python setup.py install
python train.py
In order to use your own data, you have to provide
Have a look at the load_data()
function in input_data.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/ and here (in a different format): https://github.com/kimiyoung/planetoid
You can specify a dataset as follows:
python train.py --dataset citeseer
(or by editing train.py
)
You can choose between the following models:
gcn_ae
: Graph Auto-Encoder (with GCN encoder)gcn_vae
: Variational Graph Auto-Encoder (with GCN encoder)Please cite our paper if you use this code in your own work:
@article{kipf2016variational,
title={Variational Graph Auto-Encoders},
author={Kipf, Thomas N and Welling, Max},
journal={NIPS Workshop on Bayesian Deep Learning},
year={2016}
}