Interactive Details on Demand Visual Analysis on Large Attributed Networks

查看全文 作　　者：DU [1]Xiaolei;WEI [1]Yingmei;WU [2]Lingda 机构地区：[1]College of Information System and Management,National University of Defense Technology;[2]The Key Laboratory of the Academy of Equipment,Equipment Academy 出　　处：《Chinese Journal of Electronics》索引2018年第27卷第5期,共10页 基　　金：supported by the National Natural Science Foundation of China(No.61402487) 摘　　要：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. Ma jor 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 ma jor structures, and discovering hidden information in networks. 关 键 词：网络结构 视觉分析 归因 交互 结构动态 层次结构 相互作用 挑战性