https://github.com/divelab/lgcn

https://github.com/divelab/lgcn

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/divelab/lgcn
• Owner: divelab
• License: gpl-3.0
• Created: 2018-02-08T00:28:50.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-06-16T21:17:39.000Z (over 4 years ago)
• Last Synced: 2024-05-19T00:37:40.929Z (7 months ago)
• Language: Python
• Size: 442 KB
• Stars: 136
• Watchers: 3
• Forks: 67
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• graph-networks - Large-Scale Learnable Graph Convolutional Networks

README

        
# Large-Scale Learnable Graph Convolutional Networks(LGCN)

Created by [Hongyang Gao](http://people.tamu.edu/~hongyang.gao/), [Zhengyang Wang](http://people.tamu.edu/~zhengyang.wang/) and [Shuiwang Ji](http://people.tamu.edu/~sji/) at Texas A&M University.

Accepted by KDD18.

## Introduction

Large-Scale Learnable Graph Convolutional Networks provide an efficient way (LGCL and LGCN) for learnable graph convolution.

Detailed information about LGCL and LGCN is provided in (https://dl.acm.org/citation.cfm?id=3219947).

## Methods

In this work, we propose the learnable graph convolution layer

(LGCL). Based on LGCL. We propose the learnable graph

convolutional networks.

### Learnable Graph Convolution Layer

![lgcl](./doc/layer.png)

### Learnable graph Convolutional Networks

![lgcn](./doc/model.png)

### Batch Training

![batch](./doc/batch.png)

## Citation

If using this code, please cite our paper.

```

@inproceedings{gao2018large,

  title={Large-Scale Learnable Graph Convolutional Networks},

  author={Gao, Hongyang and Wang, Zhengyang and Ji, Shuiwang},

  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},

  pages={1416--1424},

  year={2018},

  organization={ACM}

}

```

## Start training

After configure the network, we can start to train. Run

```

python main.py

```

The training results on Cora dataset will be displayed.

## Results

| Models    | Cora  | Citeseer | Pubmed |

|-----------|-------|----------|--------|

| DeepWalk  | 67.2% | 43.2%    | 65.3%  |

| Planetoid | 75.7% | 64.7%    | 77.2%  |

| Chebyshev | 81.2% | 69.8%    | 74.4%  |

| GCN       | 81.5% | 70.3%    | 79.0%  |

| LGCN      |83.3 ± 0.5% | 73.0 ± 0.6% | 79.5 ± 0.2% |