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https://github.com/tkipf/gae
Implementation of Graph Auto-Encoders in TensorFlow
https://github.com/tkipf/gae
Last synced: 10 days ago
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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 (23 days ago)
- Language: Python
- Size: 4.97 MB
- Stars: 1,644
- Watchers: 35
- Forks: 351
- Open Issues: 46
-
Metadata Files:
- Readme: README.md
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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) -- optionalHave 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}
}
```