Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs

As a powerful framework for graph representation learning, Graph Neural Networks (GNNs) have garnered significant attention in recent years. However, to the best of our knowledge, there has been no formal analysis of the logical expressiveness of GNNs as Boolean node classifiers over multi-relational graphs, where each edge carries a specific relation type. In this paper, we investigate F OC 2 , a fragment of first-order logic with two variables and counting quantifiers. On the negative side, we demonstrate that the R 2 -GNN architecture, which extends the local message passing GNN by incorporating global readout, fails to capture F OC 2 classifiers in the general case. Nevertheless, on the positive side, we establish that R 2 -GNN models are equivalent to F OC 2 classifiers under certain restricted yet reasonable scenarios. To address the limitations of R 2 -GNN regarding expressiveness, we propose a simple graph transformation technique, akin to a preprocessing step, which can be executed in linear time. This transformation enables R 2 -GNN to effectively capture any F OC 2 classifiers when applied to the "transformed" input graph. Moreover, we extend our analysis of expressiveness and graph transformation to temporal graphs, exploring several temporal GNN architectures and providing an expressiveness hierarchy for them. To validate our findings, we implement R 2 -GNN and the graph transformation technique and conduct empirical tests in node classification tasks against various well-known GNN architectures that support multi-relational or temporal graphs. Our experimental results consistently demonstrate that R 2 -GNN with the graph transformation outperform the baseline methods on both synthetic and real-world datasets. The code is available at https://github.com/hdmmblz/multi-graph . * Equal contribution, listed in alphabetical order. 37th Conference on Neural Information Processing Systems (NeurIPS 2023).