Modularity and community structure in networks
Modularity and community structure in networks
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DOI：
10.1073/pnas.0601602103
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发表时间：
2006-06-06
影响因子：
11.1
通讯作者：
Newman, M. E. J.
中科院分区：
文献类型：
--
作者：
Newman, M. E. J.
在科学领域中，许多受关注的网络，包括社交网络、计算机网络以及代谢和调控网络，都被发现会自然地划分成社区或模块。检测和描述这种社区结构的问题是网络系统研究中突出的问题之一。一种非常有效的方法是在网络可能的划分上优化被称为“模块度”的质量函数。在此我表明，模块度可以根据网络的一个特征矩阵（我称之为模块度矩阵）的特征向量来表示，并且这种表达式引出了一种用于社区检测的谱算法，该算法在更短的运行时间内返回的结果质量明显高于竞争方法。我通过将该方法应用于几个已发表的网络数据集来说明它。
Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.