  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.
