The Readability of Path-Preserving Clusterings of Graphs

被引:24
作者
Archambault, Daniel [1 ,4 ]
Purchase, Helen C. [2 ]
Pinaud, Bruno [1 ,3 ]
机构
[1] INRIA Bordeaux Sud Ouest, Bordeaux, France
[2] Univ Glasgow, Glasgow G12 8QQ, Lanark, Scotland
[3] Univ Bordeaux 1, LaBRI, CNRS, UMR 5800, F-33405 Talence, France
[4] UCD Dublin, Clique Strateg Res Cluster, Dublin, Ireland
关键词
G.2.2 [Discrete Mathematics]: Graph Theory - Graph Algorithms; H.1.2 [Information Systems]: User/Machine Systems - Human Factors;
D O I
10.1111/j.1467-8659.2009.01683.x
中图分类号
TP31 [计算机软件];
学科分类号
081205 [计算机软件];
摘要
Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path-preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs. In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced.
引用
收藏
页码:1173 / 1182
页数:10
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