Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization
We present a novel approach for graph classification based on tabularizing graph data via new variants of the Weisfeiler-Leman algorithm and then applying methods for tabular data. The variants are obtained by modifying the underlying logical framework, and we establish a precise theoretical characterization of their expressive power using a novel generalization of the bisimulation game for generalized quantifiers. We then test our method on 14 datasets that span a range of application domains. The experiments demonstrate that on datasets with up to 40 000 samples, our approach generally matches the predictive performance of graph neural networks and graph transformers, without requiring a GPU or extensive hyperparameter tuning. Even when our method's tuning time is included and the baselines' is not, our method is 5-20 times faster. When tuning time is included for all methods, the gap is significantly greater in favour of our method.