UTCS: Effective Unsupervised Temporal Community Search with Pre-training of Temporal Dynamics and Subgraph Knowledge

• URL: http://arxiv.org/abs/2506.02784v1
• Date: Tue, 03 Jun 2025 12:11:34 GMT
• Title: UTCS: Effective Unsupervised Temporal Community Search with Pre-training of Temporal Dynamics and Subgraph Knowledge
• Authors: Yue Zhang, Yankai Chen, Yingli Zhou, Yucan Guo, Xiaolin Han, Chenhao Ma,
• Abstract summary: In many real-world applications, the evolving relationships between entities can be modeled as temporal graphs, where each edge has a timestamp representing the interaction time.<n>Traditional methods typically require predefined subgraph structures, which are not always known in advance.<n>We propose an effective textbfUncontain textbfTemporal textbfCommunity textbfSearch with pre-training of temporal dynamics and subgraph knowledge model.
• Score: 15.006782872246044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many real-world applications, the evolving relationships between entities can be modeled as temporal graphs, where each edge has a timestamp representing the interaction time. As a fundamental problem in graph analysis, {\it community search (CS)} in temporal graphs has received growing attention but exhibits two major limitations: (1) Traditional methods typically require predefined subgraph structures, which are not always known in advance. (2) Learning-based methods struggle to capture temporal interaction information. To fill this research gap, in this paper, we propose an effective \textbf{U}nsupervised \textbf{T}emporal \textbf{C}ommunity \textbf{S}earch with pre-training of temporal dynamics and subgraph knowledge model (\textbf{\model}). \model~contains two key stages: offline pre-training and online search. In the first stage, we introduce multiple learning objectives to facilitate the pre-training process in the unsupervised learning setting. In the second stage, we identify a candidate subgraph and compute community scores using the pre-trained node representations and a novel scoring mechanism to determine the final community members. Experiments on five real-world datasets demonstrate the effectiveness.

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