Robustness of community structure in networks
Robustness of community structure in networks
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DOI：
10.1103/physreve.77.046119
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发表时间：
2008-04-01
影响因子：
2.4
通讯作者：
Newman, M. E. J.
中科院分区：
文献类型：
--
作者：
Karrer, Brian;Levina, Elizaveta;Newman, M. E. J.
社区结构的发现是网络数据分析中的一个常见挑战。人们已经提出了许多方法来寻找群落结构，但很少有人提出来确定所发现的结构是否具有统计学意义，或者相反，它是否可能纯粹是偶然的结果。在本文中，我们表明，社区结构的意义可以有效地量化测量其鲁棒性的小扰动网络结构。我们提出了一个合适的方法扰动网络和测量由此产生的社区结构的变化，并使用它们来评估社区结构在各种网络，真实的和计算机生成的意义。
The discovery of community structure is a common challenge in the analysis of network data. Many methods have been proposed for finding community structure, but few have been proposed for determining whether the structure found is statistically significant or whether, conversely, it could have arisen purely as a result of chance. In this paper we show that the significance of community structure can be effectively quantified by measuring its robustness to small perturbations in network structure. We propose a suitable method for perturbing networks and a measure of the resulting change in community structure and use them to assess the significance of community structure in a variety of networks, both real and computer generated.