Interactive Details on Demand Visual Analysis on Large Attributed Networks

DU Xiaolei; WEI Yingmei; WU Lingda

doi:10.1049/cje.2017.08.014

Volume 27 Issue 5

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DU Xiaolei, WEI Yingmei, WU Lingda, “Interactive Details on Demand Visual Analysis on Large Attributed Networks,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 900-909, 2018, doi: 10.1049/cje.2017.08.014

Citation:

DU Xiaolei, WEI Yingmei, WU Lingda, “Interactive Details on Demand Visual Analysis on Large Attributed Networks,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 900-909, 2018, doi: 10.1049/cje.2017.08.014

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Interactive Details on Demand Visual Analysis on Large Attributed Networks

doi: 10.1049/cje.2017.08.014

1.
College of Information System and Management, National University of Defense Technology, Changsha 410073, China;
2.
The Key Laboratory of the Academy of Equipment, Equipment Academy, Beijing 101416, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61402487).

More Information

Corresponding author: WEI Yingmei (corresponding author) was born in 1972. She received the B.S., M.S., and Ph.D. degrees in National University of Defense Technology, Changsha, China. She is the professor at the same university. Her research interests include multimedia information systems, information visualization and visual analysis techniques. (Email:weiyingmei126@126.com)
Received Date: 2017-03-01
Rev Recd Date: 2017-06-05
Publish Date: 2018-09-10

Abstract

Abstract

Increasing scale leaves a challenging problem for visualizing large attributed networks. This paper proposes a details on demand approach for exploratory visual analysis on large attributed networks. Major structures are located and emphasized at each level, providing clues for user observation. The detailed subnet structure emerges gradually through the exploration process. Our method dynamically aggregates network with consideration of both structural and attribute properties. It allows a flexible control of the hierarchy structure. A userspecified interaction strategy is introduced to enable users to customize the analysis flow according to different analytic tasks. Case studies demonstrate that the proposed method is effective in extracting global knowledge, locating major structures, and discovering hidden information in networks.
- Large attributed networks,
- Visual analytics,
- Content-based approach,
- Details on demand

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References(27)

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