Scaling Higher-Order Graph Learning with Maximal Clique Complexes 事件

Scaling Higher-Order Graph Learning with Maximal Clique Complexes arXiv:2605.31373v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poor scalability. We introduce simplified and factored cellular Weisfeiler Leman tests (sCWL and fCWL), which preserve the expressivity of the CWL test while improving computational efficiency. We further i