Created by Hongyang Gao, Zhengyang Wang and Shuiwang Ji at Texas A&M University.
Accepted by KDD18.
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).
In this work, we propose the learnable graph convolution layer (LGCL). Based on LGCL. We propose the learnable graph convolutional networks.
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}
}
After configure the network, we can start to train. Run
python main.py
The training results on Cora dataset will be displayed.
| 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% |