Light Dynamic Graph Learning on Temporal Networks

doi:10.1145/3745024

Light Dynamic Graph Learning on Temporal Networks

2025-08-08

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DOI：10.1145/3745024

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Dynamic graph learning on temporal networks aims to understand the continuous evolution pattern of networks, with an important application on forecasting the future temporal network. Existing methods mainly focus on modeling the structural and temporal features, with recent research interest shifting toward considering the structural correlations between nodes through their neighbor co-occurrences. Though satisfactory performance has been achieved, there still remain several limitations: (1) the deviation of investigated scenarios from real-world applications, since most previous researches concentrate on special cases of multigraphs with abundant repeat edges; (2) the insufficient computational efficiency of modeling the structural features, since the existing neighbor co-occurrence scheme fails to consider explicit structural correlations between nodes and suffers from a time-consuming pairwise encoding strategy; (3) the unsatisfying prediction accuracy due to inadequate modeling of temporal features, since each neighbor’s historical temporal features and the temporal domain shifting with network evolving are both neglected.

Keywords:

temporal networks

dynamic graph learning

neighbor co-occurrence

structural correlations

temporal feature modeling

Light Dynamic Graph Learning on Temporal Networks

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