1. State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China 2. Department of Computer Science and Engineering, Arizona State University, Arizona 85287, America 3. Tong Ji Intelligent Big Data Visualisation Lab (iDVx Lab), TongJi University, Shanghai 200082, China 4. Department of Computer Science and Technology, Zhengzhou University, Zhengzhou 450001, China
Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time. They are common in multimedia applications. Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed. In this paper, we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks. We conclude features of rare categories and two types of anomalies of rare categories. Then we present a novel rare category detection method, called DIRAD, to detect rare category candidates with anomalies. We develop a prototype system called iNet, which integrates two major visualization components, including a glyph-based rare category identifier, which helps users to identify rare categories among detected substructures, a major view, which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes. Evaluations, including an algorithm performance evaluation, a case study, and a user study, are conducted to test the effectiveness of proposed methods.
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