高级检索

基于内容重要性边捆绑的图可视化算法

Content Importance Based Edge Bundling for Graph Visualization

  • 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.

     

    Abstract: 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, which resulted in decrease of readability. To solve this problem, this paper proposes an edge bundling algorithm based on 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 and edges. Finally, an improved force-directed edge bundling algorithm is used to bundle those edge clusters. The edges with high importance are bundled into separate edge clusters on the premise of reducing visual clutter. Experiments show that, the algorithm can effectively reduce the influence between edge clusters that belonged to different structures in edge bundling, thus ensure connections in different structures to be properly bundled and high-level structure in graph displays more clearly.

     

/

返回文章
返回