HiMap: AdaDtive Visualization of Larae-Scale Online Social Networks
Visualizing large-scale online social network is a challenging yet essential task. This paper presents HiMap, a system that visualizes it by clustered graph via hierarchical grouping and summarization. HiMap employs a novel adaptive data loading technique to accurately control the visual density of each graph view, and along with the optimized layout algorithm and the two kinds of edge bundling methods, to effectively avoid the visual clutter commonly found in previous social network visualization tools. HiMap also provides an integrated suite of interactions to allow the users to easily navigate the social map with smooth and coherent view transitions to keep their momentum. Finally, we confirm the effectiveness of HiMap algorithms through graph-travesal based evaluations.
adaptive visualization clustered graph social network visualization
Lei Shi Nan Cao Shixia Liu Weihong Qian Li Tan Guodong Wang Jimeng Sun Ching-Yung Lin
IBM China Research Laboratory Tsinghua University IBM T.J.Watson Research Center
国际会议
IEEE Pacific Visualization Symposium 2009(2009 IEEE太平洋可视化研讨会)
北京
英文
41-48
2009-04-29（万方平台首次上网日期，不代表论文的发表时间）