Henry Nathalie, Fekete Jean-Daniel, McGuffin Michael J
INRIA Futurs/University Paris-Sud, France.
IEEE Trans Vis Comput Graph. 2007 Nov-Dec;13(6):1302-9. doi: 10.1109/TVCG.2007.70582.
The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.
随着硬件能力使分析大型网络变得可行且许多新数据集可用,可视化大型社交网络的需求日益增长。不幸的是,现有系统中的可视化无法令人满意地解决网络全局结构可读性与局部社区详细分析可读性这一基本困境。为解决此问题,我们提出了NodeTrix,一种网络的混合表示形式,它结合了两种传统表示形式的优点:节点链接图用于展示网络的全局结构,而网络的任意部分可显示为邻接矩阵以更好地支持社区分析。一个关键贡献是一组交互技术。这些技术允许分析师通过在节点链接和矩阵形式之间拖移选择来创建NodeTrix可视化,并灵活操作NodeTrix表示形式以探索数据集并创建其发现的有意义的总结可视化。最后,我们展示一个将NodeTrix应用于InfoVis 2004合著数据集分析的案例研究,以说明NodeTrix作为探索工具和有效结果交流手段的能力。