# tkipf/gae

Implementation of Graph Auto-Encoders in TensorFlow

repo name | tkipf/gae |

repo link | https://github.com/tkipf/gae |

homepage | |

language | Python |

size (curr.) | 5216 kB |

stars (curr.) | 924 |

created | 2017-06-21 |

license | MIT License |

# Graph Auto-Encoders

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:

- Link prediction in large-scale relational data: M. Schlichtkrull & T. N. Kipf et al., Modeling Relational Data with Graph Convolutional Networks (2017),
- Matrix completion / recommendation with side information: R. Berg et al., Graph Convolutional Matrix Completion (2017).

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)

## Installation

```
python setup.py install
```

## Requirements

- TensorFlow (1.0 or later)
- python 2.7
- networkx
- scikit-learn
- scipy

## Run the demo

```
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), and
- an N by D feature matrix (D is the number of features per node) – optional

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`

)

## Models

You can choose between the following models:

`gcn_ae`

: Graph Auto-Encoder (with GCN encoder)`gcn_vae`

: Variational Graph Auto-Encoder (with GCN encoder)

## Cite

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}
}
```