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arXiv · 2312.07983

Multi-perspective Feedback-attention Coupling Model for Continuous-time Dynamic Graphs

Xiaobo Zhu·Yan Wu·Zhipeng Li·Hailong Su·Jin Che·Zhanheng Chen·Liying Wang
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Abstract

Recently, representation learning over graph networks has gained popularity, with various models showing promising results. Despite this, several challenges persist: 1) most methods are designed for static or discrete-time dynamic graphs; 2) existing continuous-time dynamic graph algorithms focus on a single evolving perspective; and 3) many continuous-time dynamic graph approaches necessitate numerous temporal neighbors to capture long-term dependencies. In response, this paper introduces the Multi-Perspective Feedback-Attention Coupling (MPFA) model. MPFA incorporates information from both evolving and raw perspectives, efficiently learning the interleaved dynamics of observed processes. The evolving perspective employs temporal self-attention to distinguish continuously evolving temporal neighbors for information aggregation. Through dynamic updates, this perspective can capture long-term dependencies using a small number of temporal neighbors. Meanwhile, the raw perspective utilizes a feedback attention module with growth characteristic coefficients to aggregate raw neighborhood information. Experimental results on a self-organizing dataset and seven public datasets validate the efficacy and competitiveness of our proposed model.

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Xiaobo Zhu, Yan Wu, Zhipeng Li, Hailong Su, Jin Che, Zhanheng Chen, Liying Wang. 2023-12-13. Multi-perspective Feedback-attention Coupling Model for Continuous-time Dynamic Graphs. https://arxiv.org/abs/2312.07983

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