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iNet: visual analysis of irregular transition in multivariate dynamic networks

Abstract

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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Acknowledgements

This work was supported by National Key Research and Development Program (2018YFB0904503), the National Natural Science Foundation of China (Grant Nos. 61772456, U1866602, 61761136020, U1736109).

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Correspondence to Wei Chen.

Additional information

Dongming Han is currently working towards the PhD degree with the State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, China. His research interests include visualization and visual analytics.

Jiacheng Pan is currently working towards the PhD degree with the State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, China. He is a fan of visualization and visual analytics.

Rusheng Pan is currently working towards the PhD degree with the State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, China. His research interests include visualization and visual analytics.

Dawei Zhou is a PhD candidate at Department of Computer Science and Engineering, Arizona State University, USA. His research interests are rare category detection, multi-view learning, and spatiotemporal learning.

Nan Cao is a professor at TongJi University in China, with the joint appointment at both College of Design and Innovation and College of Software Engineering. His primary expertise and research interests are information visualization and visual analysis.

Jingrui He is an assistant professor of computer science at Arizona State University, USA. Her research interests are heterogeneous machine learning, rare category analysis, active learning and semisupervised learning, with applications in social network analysis, healthcare, and manufacturing.

Mingliang Xu is a professor in the School of Information Engineering of Zhengzhou University, China. His current research interests include computer graphics, multimedia, artificial intelligence and virtual reality.

Wei Chen is a professor in State Key Lab of CAD&CG at Zhejiang University, China. He has performed research in visualization and visual analysis. His current research interests include visualization, visual analytics and bio-medical image computing.

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Han, D., Pan, J., Pan, R. et al. iNet: visual analysis of irregular transition in multivariate dynamic networks. Front. Comput. Sci. 16, 162701 (2022). https://doi.org/10.1007/s11704-020-0013-1

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Keywords

  • multivariate dynamic networks
  • rare categories
  • anomaly detection
  • visual analysis