Study on Anomalous Evolution Pattern on Temporal Networks
The competitive methods for anomalous subgraphs detection have been successfully applied to tasks like event detec-tion 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 evo-lution 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 applica-tions:traffic congestion detection in urban road net works(Beijing,Tianjin,and Nanjing in China),event detection in the social net-work(Weibo)and cyber-attack detection in computer traffic network.
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