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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 ...

The method leverages graph neural networks (GNNs) to integrate representations from both the generated claim and the source document KGs, enabling fine-grained fact-checking within a single model call ...

Abstract: With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from research and industry field because of their high accuracy. However, existing ...

[WWW 2024] Graph-Skeleton: ～ 1% Nodes are Sufficient to Represent Billion-Scale Graph. [arXiv 2024] Graph Coarsening with Message-Passing Guarantees. [Pacific Symposium on Biocomputing 2023] A Graph ...