Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (7): 272-279.doi: 10.11896/jsjkx.250900118

• Database & Big Data & Data Science • Previous Articles     Next Articles

Unsupervised Dynamic Graph Change Point Detection Method Based on Variational Graph Auto-encoder

WANG Jiajun1, JIAO Pengfei1,2, ZHANG Xinxun1, LI Tianpeng3, GAO Mengzhou1   

  1. 1 School of Cyberspace Security,Hangzhou Dianzi University,Hangzhou 310018,China
    2 State Key Laboratory of Blockchain and Data Security,Zhejiang University,Hangzhou 310027,China
    3 College of Intelligence and Computing,Tianjin University,Tianjin 300072,China
  • Received:2025-09-18 Revised:2026-01-03 Online:2026-07-15 Published:2026-07-10
  • About author:WANG Jiajun,born in 2002,master.His main research interests include change point detection and network alignment.
    JIAO Pengfei,born in 1990,Ph.D,professor,is a member of CCF(No.27894M).His main research interest is complex network analysis and its applications.
  • Supported by:
    Zhejiang Provincial Science and Technology Plan(2025C01023),Zhejiang Provincial Natural Science Foundation(LMS25F030011),National Natural Science Foundation of China(62372146) and Zhejiang Provincial Key Laboratory for Sensitive Data Security Protetion and Cnofidentiality Management(2024E10048).

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Abstract: Detecting or identifying event-related change points in dynamic networks is becoming increasingly important,as structural variations in the network may correspond to changes in system functionality.However,many existing change point detection techniques fail to effectively capture node features.To address this limitation,this paper proposes an unsupervised dynamic graph change point detection model based on a Variational Graph Autoencoder(VGRCPD).The model integrates Variational Graph Autoencoders with recurrent neural networks and introduces a self-attention mechanism to compute the prior distribution from historical time steps.After obtaining graph embeddings,network snapshots are clustered into disjoint groups and arranged in temporal order.The resulting time series of cluster labels naturally indicates potential change points.Experimental results on multiple real-world and synthetic datasets demonstrate that the proposed method achieves superior performance,validating its effectiveness and potential in dynamic graph analysis.

Key words: Change point detection, Graph neural networks, Dynamic graphs, Community detection, Graph variational autoen-coder

CLC Number: 

  • TP181
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