Message passing neural networks (MPNNs) learn the representation of graph-structured data based on graph original information, including node features and graph structures, and have shown astonishing improvement in node classification tasks. However, the expressive power of MPNNs is upper bounded by the first-order Weisfeiler-Leman test and its accuracy still has room for improvement. This work studies how to improve MPNNs' expressiveness and generalizability by fully exploiting graph original information both theoretically and empirically. It further proposes a new GNN model called INGNN (INformation-enhanced Graph Neural Network) that leverages the insights to improve node classification performance. Extensive experiments on both synthetic and real datasets demonstrate the superiority (average rank 1.78) of our INGNN compared with state-of-the-art methods.

翻译：消息传递神经网络基于图原始信息（包括节点特征和图结构）学习图结构数据的表示，在节点分类任务中展现出显著的性能提升。然而，消息传递神经网络的表达能力受限于一阶Weisfeiler-Lehman测试，其准确性仍有提升空间。本文从理论和实证两个层面系统研究如何通过充分利用图原始信息来提升消息传递神经网络的表达能力和泛化能力，并提出一种新型图神经网络模型INGNN（信息增强图神经网络），该模型利用上述洞察提升节点分类性能。在合成数据集和真实数据集上的大量实验表明，与现有最优方法相比，我们的INGNN具有显著优势（平均排名1.78）。