Community structure in social and biological networks
Community structure in social and biological networks
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DOI：
10.1073/pnas.122653799
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发表时间：
2002-06-11
影响因子：
11.1
通讯作者：
Newman, MEJ
中科院分区：
文献类型：
--
作者：
Girvan, M;Newman, MEJ
最近的一些研究集中在网络系统，如社交网络和万维网的统计特性。研究人员特别关注了一些似乎是许多网络共有的性质：小世界性质，幂律度分布和网络传递性。在这篇文章中，我们强调了在许多网络中发现的另一个属性，社区结构的属性，其中网络节点以紧密结合的组连接在一起，之间只有松散的连接。我们提出了一种方法来检测这样的社区，建立在使用中心性指数，找到社区边界的想法。我们测试我们的方法在计算机生成的和真实世界的图形，其社区结构是已知的，并发现该方法检测这种已知的结构具有高灵敏度和可靠性。我们还将该方法应用于两个网络的社区结构是不太清楚的合作网络和食物网，并发现它检测到显着的和翔实的社区部门在这两种情况下。
A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known-a collaboration network and a food web-and find that it detects significant and informative community divisions in both cases.