In the realm of graph learning, there is a category of methods that conceptualize graphs as hierarchical structures, utilizing node clustering to capture broader structural information. While ...
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Abstract: For cyber-physical systems, the predictability of their physical behaviors needs to be ensured by the determinism of cyberspace. Response-time analysis (RTA) can theoretically provide this ...
Abstract: The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network (GNN) models and suggests aggregations ...
In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classification) using one ...
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