In recent years, Graph Neural Network (GNN) has bloomly progressed for its power in processing graph-based data. Most GNNs follow a message passing scheme, and their expressive power is mathematically limited by the discriminative ability of the Weisfeiler-Lehman (WL) test. Following Tinhofer's research on compact graphs, we propose a variation of the message passing scheme, called the Weisfeiler-Lehman-Tinhofer GNN (WLT-GNN), that theoretically breaks through the limitation of the WL test. In addition, we conduct comparative experiments and ablation studies on several well-known datasets. The results show that the proposed methods have comparable performances and better expressive power on these datasets.
@article{arxiv.2104.01848,
title = {Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm},
author = {Alan J. X. Guo and Qing-Hu Hou and Ou Wu},
journal= {arXiv preprint arXiv:2104.01848},
year = {2021}
}