  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.
