Visualization Symposium, IEEE Pacific (2009)
Apr. 20, 2009 to Apr. 23, 2009
Lei Shi , IBM China Research Laboratory, China
Nan Cao , IBM China Research Laboratory, China
Shixia Liu , IBM China Research Laboratory, China
Weihong Qian , IBM China Research Laboratory, China
Li Tan , IBM China Research Laboratory, China
Guodong Wang , Tsinghua University, China
Jimeng Sun , IBM T. J. Watson Research Center, USA
Ching-Yung Lin , IBM T. J. Watson Research Center, USA
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.
Weihong Qian et al., "HiMap: Adaptive visualization of large-scale online social networks," 2009 IEEE Pacific Visualization Symposium(PACIFICVIS), Beijing, 2009, pp. 41-48.