jtpils / lgcn

Large-Scale Learnable Graph Convolutional Networks(LGCN)

Created by Hongyang Gao, Zhengyang Wang and Shuiwang Ji at Washington State 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).

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%