图中聚类可视化的质量度量
A Quality Metric for Visualization of Clusters in Graphs
摘要：
传统上，图形质量指标侧重于可读性，但最近的研究表明，需要更具体的发现图形中的模式的指标。聚类分析是图分析中的一项流行任务，但还没有明确量化图的绘制如何表示其聚类结构的度量。我们定义了一个聚类质量度量标准，衡量一个图的节点链接图表示图中包含的集群。变形图形绘图的实验验证了我们的度量有效地捕捉图形绘图的视觉聚类质量的变化。然后，我们使用我们的度量来研究不同的图形绘制算法如何在各种图形中可视化集群结构;结果证实，一些专门设计用于显示集群结构的算法比其他算法表现得更好。
英文摘要：
Traditionally, graph quality metrics focus on readability, but recent studies show the need for metrics which are more specific to the discovery of patterns in graphs. Cluster analysis is a popular task within graph analysis, yet there is no metric yet explicitly quantifying how well a drawing of a graph represents its cluster structure. We define a clustering quality metric measuring how well a node-link drawing of a graph represents the clusters contained in the graph. Experiments with deforming graph drawings verify that our metric effectively captures variations in the visual cluster quality of graph drawings. We then use our metric to examine how well different graph drawing algorithms visualize cluster structures in various graphs; the results con-firm that some algorithms which have been specifically designed to show cluster structures perform better than other algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
