MIRALab-USTC / GCN4KGC

The code of paper Rethinking Graph Convolutional Networks in Knowledge Graph Completion. Zhanqiu Zhang, Jie Wang, Jieping Ye, Feng Wu. WWW 2022.

Rethinking Graph Convolutional Networks in Knowledge Graph Completion

This is the code of paper Rethinking Graph Convolutional Networks in Knowledge Graph Completion. Zhanqiu Zhang, Jie Wang, Jieping Ye, Feng Wu. WWW 2022. [arXiv]

Requirements

• python 3.7
• torch 1.8
• dgl 0.7

Reproduce the Results

Pleaes run the commands in RGCN+CompGCN+LTE/script or WGCN/script to reproduce the results.

Meaning of different options.

• rat: random adjacency tensors.
• wsi: without self-loop information.
• wni: without neighbor information.
• ss: sample set sizes for random sampled neighbors.

Citation

If you find this code useful, please consider citing the following paper.

@inproceedings{WWW22_GCN4KGC,
 author = {Zhanqiu Zhang and Jie Wang and Jieping Ye and Feng Wu},
 booktitle = {The Web Conference 2022},
 title = {Rethinking Graph Convolutional Networks in Knowledge Graph Completion},
 year = {2022}
}

Acknowledgement

We refer to the code of CompGCN, WGCN, and DGL. Thanks for their contributions.