Steen Matthew, Hayasaka Satoru, Joyce Karen, Laurienti Paul
Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jul;84(1 Pt 2):016111. doi: 10.1103/PhysRevE.84.016111. Epub 2011 Jul 26.
In recent years, community structure has emerged as a key component of complex network analysis. As more data have been collected, researchers have begun investigating changing community structure across multiple networks. Several methods exist to analyze changing communities, but most of these are limited to evolution of a single network over time. In addition, most of the existing methods are more concerned with change at the community level than at the level of the individual node. In this paper, we introduce scaled inclusivity, which is a method to quantify the change in community structure across networks. Scaled inclusivity evaluates the consistency of the classification of every node in a network independently. In addition, the method can be applied cross sectionally as well as longitudinally. In this paper, we calculate the scaled inclusivity for a set of simulated networks of United States cities and a set of real networks consisting of teams that play in the top division of American college football. We found that scaled inclusivity yields reasonable results for the consistency of individual nodes in both sets of networks. We propose that scaled inclusivity may provide a useful way to quantify the change in a network's community structure.
近年来，社区结构已成为复杂网络分析的关键组成部分。随着收集到的数据越来越多，研究人员开始研究多个网络中不断变化的社区结构。有几种方法可用于分析不断变化的社区，但其中大多数仅限于单个网络随时间的演变。此外，现有的大多数方法更关注社区层面的变化，而非单个节点层面的变化。在本文中，我们引入了缩放包容性，这是一种量化跨网络社区结构变化的方法。缩放包容性独立评估网络中每个节点分类的一致性。此外，该方法既可以用于横断面分析，也可以用于纵向分析。在本文中，我们计算了一组美国城市模拟网络以及一组由参加美国大学橄榄球顶级联赛的球队组成的真实网络的缩放包容性。我们发现，缩放包容性对于两组网络中单个节点的一致性都产生了合理的结果。我们提出，缩放包容性可能为量化网络社区结构的变化提供一种有用的方法。
