Large-Scale Learnable Graph Convolutional Networks(LGCN)
Created by Hongyang Gao, Zhengyang Wang and Shuiwang Ji 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
Learnable graph Convolutional Networks
Batch Training
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 Β± 0.5% | 73.0 Β± 0.6% | 79.5 Β± 0.2% |