关键词: 动态网络, 模式挖掘, 异常发现

Abstract: The majority of real-world complex systems can be abstracted as dynamic information networks, such as social network, co-author network and e-mail network. Dynamic information networks are temporal, complex and changeable. To understand the behavioral trend of the complex systems, it is necessary to analyze the evolution of the network structures, especially the abnormal phenomena in the evolution. Aiming at detecting the anomalies in the dynamic evolution of the network structure, this paper utilizes “roles” to capture the structural characteristics of nodes, proposes the notion of role evolving outliers (REOutliers), and proposes a pattern-based role evolving outliers detection (P-REOD) method. This method mines the frequent patterns that roles of dynamic network structure evolve over time, and evaluates the degree of a node deviating from the frequent patterns to find the REOutliers. The experimental results show that the proposed method is highly effective in discovering interesting REOutliers.

Key words: dynamic networks, pattern mining, outlier detection