HiMap:大规模在线社交网络的自适应可视化

Lei Shi, Nan Cao, Shixia Liu, Weihong Qian, Li Tan, Guodong Wang, Jimeng Sun, Ching-Yung Lin
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引用次数: 51

摘要

可视化大型在线社交网络是一项具有挑战性但又必不可少的任务。本文提出了一种通过分层分组和汇总的聚类图可视化系统HiMap。HiMap采用了一种新颖的自适应数据加载技术,精确控制每个图视图的视觉密度,并结合优化布局算法和两种边缘捆绑方法,有效避免了以往社交网络可视化工具中常见的视觉杂乱。HiMap还提供了一套集成的交互,允许用户轻松地导航社交地图,并使用平滑和连贯的视图转换来保持他们的势头。最后,我们通过基于图旅行的评估来验证HiMap算法的有效性。
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HiMap: Adaptive visualization of large-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.
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