可视化以社区为中心的网络布局

Justin Fagnan, Osmar R Zaiane, R. Goebel
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引用次数: 9

摘要

我们提出了社区边界(COMB)和社区圈(COMC)网络布局算法,这些算法的重点是揭示已发现社区的结构以及这些社区之间的关系。我们相信,在开发新的社区挖掘算法时,这些信息是至关重要的,因为它允许查看者更快地评估挖掘结果的质量,而无需诉诸大型统计表。为了实现我们的算法,我们对现有的Fruchterman-Reingold布局进行了大量修改,包括支持多尺寸的顶点、移除边界框、引入圆形边界框和一个新的分槽系统。我们的评估认为,COMB和COMC在揭示社区结构和强调社区间关系的能力方面都优于现有的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualizing community centric network layouts
We present our COMmunity Boundary (COMB) and COMmunity Circles (COMC) network layout algorithms that focus on revealing the structure of discovered communities and the relationships between these communities. We believe this information is vital when developing new community mining algorithms as it allows the viewer to more quickly assess the quality of a mining result without appealing to large tables of statistics. To implement our algorithms we have introduced numerous modifications to the existing Fruchterman-Reingold layout, including support for multi-sized vertices, removal of the bounding frame, introduction of circular bounding boxes, and a novel slotting system. Our evaluation argues that both COMB and COMC outperform existing alternatives in their ability to reveal community structure and emphasize inter-community relations.
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