Abstract: Graph neural networks have proved to be a key tool for dealing with many problems and domains, such as chemistry, natural language processing, and social networks. While the structure of the ...
Abstract: Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static ...
tl;dr: We provably improve GNN expressivity by enhancing message passing with substructure encodings. Our method allows incorporating domain specific prior knowledge and can be used as a drop-in ...
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