Study on Anomalous Evolution Pattern on Temporal Networks

• Published in: Jisuanji kexue
• Main Author: WU Nannan, GUO Zehao, ZHAO Yiming, YU Wei, SUN Ying, WANG Wenjun
• Format: Article
• Language: Chinese
• Published: Editorial office of Computer Science 2024-06-01
• Subjects: anomaly detection|subgraph|dynamic graph|non-parametric scan statistics
• Online Access: https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2024-51-6-118.pdf

The competitive methods for anomalous subgraphs detection have been successfully applied to tasks like event detection in social networks,traffic congestion detection in road networks,etc.However,few studies have been initiated in the dynamic evolution of anomalous subgraphs in attributed graphs.For multiple anomalous subgraph evolving pattern,it is the first dynamic graph-based study to capture multi-anomalies connected on time intervals.This study proposes an approach,namely dynamic evolution of multiple anomalous subgraphs scanning(DE-MASS),to detect the most anomalous evolutionary pattern,which consists of multiple anomalous subgraphs on attributed graphs.The DE-MASS outperforms the competitive baselines in the Weibo real dataset,computer traffic real dataset,and captures the evolution patterns of anomalous subgraphs on three real-world applications:traffic congestion detection in urban road networks(Beijing,Tianjin,and Nanjing in China),event detection in the social network(Weibo)and cyber-attack detection in computer traffic network.