摘要
图可视化中边捆绑算法可用来减少图的视觉混乱并增强图的高等级结构显示.针对一般边捆绑过程中,因属于不同结构的边可能相互捆绑而降低捆绑结果可读性的问题,提出一种基于图的内容重要度的边捆绑算法.首先使用关联边提取和关联度估值算法,从包含大量的点、线连接的图中提取出可反映图的高等级结构的边簇信息,然后使用改进的力引导捆边算法对边簇进行捆绑,达到在减少图的视觉混乱同时将重要性较高的连接捆绑到独立的边簇中的目的.实验结果表明,该算法可以有效地降低不同结构边簇在边捆绑中的相互影响,从而保证属于不同结构的重要连接可以得到恰当的捆绑进而使得图中高等级结构的显示更清晰.
Edge bundling is widely used to reduce visual clutter and enhance high-level structure of graph in visualization.However, in some cases edges that belonged to different structures may be bundled together, whichresulted in decrease of readability. To solve this problem, this paper proposes an edge bundling algorithm basedon content importance. Firstly, a relation measuring algorithm is used to obtain the information of each edge.Then, those edge clusters which can reflect the high-level structure of graph are explored from massive points andedges. Finally, an improved force-directed edge bundling algorithm is used to bundle those edge clusters. Theedges with high importance are bundled into separate edge clusters on the premise of reducing visual clutter. Experimentsshow that, the algorithm can effectively reduce the influence between edge clusters that belonged todifferent structures in edge bundling, thus ensure connections in different structures to be properly bundled andhigh-level structure in graph displays more clearly.
作者
路强
马坤乐
Lu Qiang;Ma Kunle(School of Computer and Information, Hefei University of Technology, Hefei 230009)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2016年第11期1899-1905,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61472115)
安徽高校省级自然科学研究项目(KJ2014ZD27)
关键词
边捆绑
边簇
关联度估值
力引导
视觉混乱
edge bundling
edge clusters
relation measuring
force-directed
visual clutter
