Department of Computer Science, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, USA.
IEEE Trans Vis Comput Graph. 2013 Jun;19(6):1034-47. doi: 10.1109/TVCG.2012.172.
Community structure is an important characteristic of many real networks, which shows high concentrations of edges within special groups of vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships among communities, identifying attribute-structure relationships, and selecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks. The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated, uncluttered visualizations, and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.
社区结构是许多真实网络的一个重要特征，它表现为顶点的特殊群组内具有高浓度的边，而这些群组之间的边浓度较低。社区相关的图分析，如发现社区之间的关系、识别属性-结构关系以及选择具有所需结构特征和属性的大量顶点，是这些网络中知识发现的常见任务。杂乱无章和缺乏交互性常常阻碍了传统图可视化技术在这些任务中的应用。在本文中，我们提出了一种新的图分析方法 PIWI，用于解决这些任务。PIWI 没有使用节点-链接图（NLD），而是基于图社区结构提供协调、整洁的可视化和新颖的交互方式。详细讨论了这项新技术的新颖特征、适用性和局限性。已经对包含数千个顶点的真实图进行了一系列案例研究和初步用户研究，这些研究为 PIWI 在社区相关任务中的有用性提供了支持证据。
