利用Tinhofer Algoorithm改进图形神经网络的表达力 (Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm)

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.

翻译：近年来,图形神经网络(GNN)在处理基于图形的数据方面的力量取得了蓬勃的进步。大多数GNN都遵循信息传递计划,其表达力在数学上受到Weisfeiler-Lehman(WL)测试的歧视性能力的限制。在Tinhofer对紧凑图形的研究之后,我们建议对信息传递计划(称为Weisfeiler-Lehman-Tinhofer GNN(WLT-GNN))进行变通,这个计划理论上突破了WL测试的局限性。此外,我们还对几个众所周知的数据集进行了比较实验和模拟研究。结果显示,拟议的方法在这些数据集上具有可比的性能和更好的表达力。