Cui Weiwei, Zhou Hong, Qu Huamin, Wong Pak Chung, Li Xiaoming
Hong Kong University of Science and Technology.
IEEE Trans Vis Comput Graph. 2008 Nov-Dec;14(6):1277-84. doi: 10.1109/TVCG.2008.135.
Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edge-clustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edge-clustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edge-clustering methods, our approach is intuitive, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method.
图表已被广泛用于对数据之间的关系进行建模。对于大型图表，过多的边交叉会使显示在视觉上显得杂乱，从而难以进行探索。在本文中，我们提出了一种新颖的基于几何的边聚类框架，该框架可以将边分组为束以减少整体边交叉。我们的方法使用控制网格来指导边聚类过程；通过强制所有边穿过网格上的一些控制点可以形成边束。控制网格可以基于基础图形模式手动或自动地在不同细节级别生成。用户可以通过诸如颜色和不透明度增强等几种先进的可视化技术进一步与边聚类结果进行交互。与其他边聚类方法相比，我们的方法直观、灵活且高效。在一些大型图表上进行的实验证明了我们方法的有效性。
