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
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.