A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
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DOI：
10.1109/tfuzz.2013.2245135
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发表时间：
2013-12
影响因子：
11.9
通讯作者：
T. Havens;J. Bezdek;C. Leckie;K. Ramamohanarao;M. Palaniswami
中科院分区：
文献类型：
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作者：
T. Havens;J. Bezdek;C. Leckie;K. Ramamohanarao;M. Palaniswami
本文讨论了一种推广Newman-Girvan （NG）模块函数的模糊有效性指标的新公式。在社区检测研究中，NG函数作为聚类效度函数。输入数据是一个无向加权图，表示，例如，一个社会网络。集群对应于网络中社会相似的子结构。我们将模糊模块性与两种已有的模块性函数进行了比较，这些函数使用的是经过充分研究的空手道俱乐部和美国大学橄榄球数据集。
We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan (NG) modularity function. The NG function serves as a cluster validity functional in community detection studies. The input data is an undirected weighted graph that represents, e.g., a social network. Clusters correspond to socially similar substructures in the network. We compare our fuzzy modularity with two existing modularity functions using the well-studied Karate Club and American College Football datasets.