College of Information Sciences and Technology, the Pennsylvania State University, USA.
IEEE Trans Vis Comput Graph. 2011 Dec;17(12):2449-58. doi: 10.1109/TVCG.2011.247.
Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.
网络数据通常包含来自社交网络中社交联系和专业领域等各个维度的重要属性。如果这些属性呈现出树状结构，那么可视化由树和网络结构组成的复合图就会变得复杂。如何直观地揭示网络在树上的模式尚未得到充分研究。在本文中，我们提出了一种复合图模型 TreeNet，以支持在树上对网络进行多层次聚合的可视化和分析。我们还提出了一种可视化设计 TreeNetViz，以提供 TreeNet 图的多尺度和跨尺度探索和交互。TreeNetViz 使用径向、空间填充（RSF）可视化来表示树结构，使用新颖的优化算法来展示从 TreeNet 派生的聚合网络，并使用边缘捆绑技术来减少视觉复杂度。我们的圆形布局算法减少了总边交叉和边长度，并且还考虑了 TreeNet 图中的层次结构约束和边权重。这些实验表明，该算法可以减少 TreeNet 图中的视觉混乱。我们的案例研究还表明，TreeNetViz 有可能通过揭示多尺度和跨尺度的网络模式来支持复合图的分析。
