NDLI: HiMap: Adaptive visualization of large-scale online social networks

Content Provider	IEEE Xplore Digital Library
Author	Lei Shi Nan Cao Shixia Liu Weihong Qian Li Tan Guodong Wang Jimeng Sun Ching-Yung Lin
Copyright Year	2009
Description	Author affiliation: IBM T. J. Watson Research Center, USA (Jimeng Sun; Ching-Yung Lin) \|\| IBM China Research Laboratory, China (Lei Shi; Nan Cao; Shixia Liu; Weihong Qian; Li Tan) \|\| Tsinghua University, China (Guodong Wang)
Abstract	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.
Starting Page	41
Ending Page	48
File Size	989690
Page Count	8
File Format	PDF
ISBN	9781424444045
DOI	10.1109/PACIFICVIS.2009.4906836
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Navigation Social network services Laboratories adaptive visualization social network visualization Data visualization Clustering algorithms clustered graph Animation MySpace Large-scale systems IP networks Facebook
Content Type	Text
Resource Type	Article

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