https://github.com/tkipf/gae

Implementation of Graph Auto-Encoders in TensorFlow
https://github.com/tkipf/gae

Last synced: 10 days ago
JSON representation

Implementation of Graph Auto-Encoders in TensorFlow

• Host: GitHub
• URL: https://github.com/tkipf/gae
• Owner: tkipf
• License: mit
• Created: 2017-06-21T07:48:31.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-01-03T12:56:37.000Z (almost 5 years ago)
• Last Synced: 2024-10-16T09:05:39.652Z (24 days ago)
• Language: Python
• Size: 4.97 MB
• Stars: 1,644
• Watchers: 35
• Forks: 351
• Open Issues: 46
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-graph-based-semi-supervised-learning - code
• StarryDivineSky - tkipf/gae

README

        
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](https://arxiv.org/abs/1611.07308), 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. 

![(Variational) Graph Auto-Encoder](figure.png)

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](https://arxiv.org/abs/1703.06103) (2017),

* Matrix completion / recommendation with side information: R. Berg et al., [Graph Convolutional Matrix Completion](https://arxiv.org/abs/1706.02263) (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](https://arxiv.org/abs/1609.02907), ICLR (2017). 

A high-level introduction is given in our blog post:

Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016)

## Installation

```bash

python setup.py install

```

## Requirements

* TensorFlow (1.0 or later)

* python 2.7

* networkx

* scikit-learn

* scipy

## 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), 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:

```bash

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}

}

```