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Structure-reinforced Transformer for Dynamic Graph Representation Learning with Edge Temporal States
April 4, 2024, 4:42 a.m. | Shengxiang Hu, Guobing Zou, Song Yang, Shiyi Lin, Bofeng Zhang, Yixin Chen
cs.LG updates on arXiv.org arxiv.org
Abstract: The burgeoning field of dynamic graph representation learning, fuelled by the increasing demand for graph data analysis in real-world applications, poses both enticing opportunities and formidable challenges. Despite the promising results achieved by recent research leveraging recurrent neural networks (RNNs) and graph neural networks (GNNs), these approaches often fail to adequately consider the impact of the edge temporal states on the strength of inter-node relationships across different time slices, further overlooking the dynamic changes in …
abstract analysis applications arxiv challenges cs.lg data data analysis demand dynamic edge graph graph data graph neural networks graph representation networks neural networks opportunities recurrent neural networks representation representation learning research results temporal transformer type world
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