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DyG-WM is proposed, the first framework to jointly predict future node and relation representations from temporal context by answering three questions: what has evolved, how it evolved, and what will evolve next.
Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hypergraph learning methods such as hypergraph neural networks have demonstrated remarkable predictive performance, most existing approaches rely on black-box message-pas...
Graph distribution shifts between training and test graphs pose severe challenges to the generalization of graph neural networks (GNNs). In real-world deployment, application environments are continuously evolving, while retraining or redesigning GNNs is often costly and impractical. In light of this, test-time adaptat...
A novel framework of temporal memory-aware Online Test-Time Adaptation on Dynamic Graphs, named DGOTTA, to effectively adapt well-trained DGNNs during test time and significantly improves generalization under diverse distribution shifts and multiple model architectures is proposed.
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