Simple probabilistic algorithm for detecting community structure
Simple probabilistic algorithm for detecting community structure
复制标题
用于检测社区结构的简单概率算法
DOI：
10.1103/physreve.79.036111
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发表时间：
2009-03-01
影响因子：
2.4
通讯作者：
Xiao, Lan
中科院分区：
文献类型：
--
作者：
Ren, Wei;Yan, Guiying;Xiao, Lan
随着可用的社交和生物网络数量的不断增加，检测网络社区结构的问题变得越来越重要，这是分析这些数据的第一步。社区结构一般认为是同一社区内的节点往往有较多的边，不同社区内的节点往往有较少的边。我们提出了一种简单的概率算法来检测社区结构，该算法采用期望最大化（SPAEM）。我们还给出了基于最小描述长度的标准来确定最佳社区数量。 SPAEM 可以检测重叠节点并处理加权网络。通过测试模拟数据和一些众所周知的数据集，事实证明它是强大且有效的。
With the growing number of available social and biological networks, the problem of detecting the network community structure is becoming more and more important which acts as the first step to analyze these data. The community structure is generally regarded as that nodes in the same community tend to have more edges and less if they are in different communities. We propose a simple probabilistic algorithm for detecting community structure which employs expectation-maximization (SPAEM). We also give a criterion based on the minimum description length to identify the optimal number of communities. SPAEM can detect overlapping nodes and handle weighted networks. It turns out to be powerful and effective by testing simulation data and some widely known data sets.