Orginal Article
1. State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China
2. Zhejiang Lab, Hangzhou 311100, China
3. Department of Computer Science and Engineering, Arizona State University, Tempe 85281, USA
4. Intelligent Big Data Visualisation Lab, Tongji University, Shanghai 200082, China
5. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
6. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China |
Available online: 2020-05-13
Abstract
A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.
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