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OnionGraph:层次拓扑与属性多变量，网络可视化
Lei Shia, Qi Liaob, Hanghang Tongc, Yifan Hud, Chaoli Wange, Chuang Linf, Weihong Qiang
aACT&BDBC, Department of Computer Science & Engineering, Beihang University, Beijing 100191, China  bDepartment of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, United States  cDepartment of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, United States  dYahoo Labs, New York, NY 10036, United States  eDepartment of Computer Science & Engineering, University of Notre Dame, Notre Dame, IN 46556, United States fDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, China gAlibaba Cloud, Beijing 100102, China
OnionGraph: Hierarchical topology+attribute multivariate network visualization
Lei Shia, Qi Liaob, Hanghang Tongc, Yifan Hud, Chaoli Wange, Chuang Linf, Weihong Qiang#br#
aACT&BDBC, Department of Computer Science & Engineering, Beihang University, Beijing 100191, China  bDepartment of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, United States  cDepartment of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, United States  dYahoo Labs, New York, NY 10036, United States  eDepartment of Computer Science & Engineering, University of Notre Dame, Notre Dame, IN 46556, United States fDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, China gAlibaba Cloud, Beijing 100102, China
 全文: PDF 
摘要： 层次抽象是一种处理大规模网络的可扩展策略。现有的可视化方法根据结点属性或网络拓扑将网络结点聚合成层次结构，每种方法各有优点。但之前的系统很少能兼具两者的长处。 本文介绍了OnionGraph，这是一个对异构多元网络进行探索性可视化分析的集成框架。OnionGraph可基于结点属性拓扑或两者的分层组合来聚合结点。这些聚合可以在焦点+上下文交互模型下被拆分、合并和过滤，也可以通过基于信息理论的导航方法自动遍历。包含结点子集的结点聚合以洋葱隐喻的方式进行展示，呈现出抽象的层次和细节。 我们通过三个真实案例中对OnionGraph工具进行了评估。性能实验表明，在商用台式机上，我们的方法可以扩展到拥有百万结点的网络，与此同时保持交互分析的性能。
关键词： 多元网络可视化;  层次抽象;  焦点+上下文;  熵    
Abstract: Hierarchical abstraction is a scalable strategy to deal with large networks. Existing visualization methods have allowed to aggregate the network nodes into hierarchies based on the node attributes or network topology, each of which has its own advantage. Very few previous system has the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for the exploratory visual analysis of heterogeneous multivariate networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a hierarchical combination of both. These aggregations can be split, merged and filtered under the focus+context interaction model, or automatically traversed by the information-theoretic navigation method. Node aggregations that contain subsets of nodes are displayed by the onion metaphor, indicating the level and details of the abstraction. We have evaluated the OnionGraph tool in three real-world cases. Performance experiments demonstrate that on a commodity desktop, our method can scale to million-node networks while preserving the interactivity for analysis.
Key words: Multivariate network visualization    Hierarchical abstraction    Focus+context    Entropy
通讯作者: Lei Shi ACT&BDBC, Department of Computer Science & Engineering, Beihang University, Beijing 100191, China     E-mail: leishi@buaa.edu.cn
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Lei Shi
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引用本文:

Lei Shi, Qi Liao, Hanghang Tong, Yifan Hu, Chaoli Wang, Chuang Lin, Weihong Qian. OnionGraph: Hierarchical topology+attribute multivariate network visualization. Vis Inf, 10.1016/j.visinf.2020.01.002.

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http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2020.01.002    或    http://www.zjujournals.com/vi/CN/Y2020/V4/I1/43

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