一种基于模块化的重叠社团结构检测算法

Kui Meng, Gongshen Liu, Qiong Hu, Jianhua Li
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引用次数: 3

摘要

已经设计了许多算法来检测社交网络中的社区结构。然而,大多数算法只能有效地检测到不相交的群体。本文提出了一种新的重叠社团结构检测算法,该算法采用模块化的社团聚类方法。为了对算法进行评价,采用Newman的Modularity和Lancichinetti的NMI (Normalized Mutual Information)作为评价指标。实验结果表明,该方法能较好地适应实际的重叠群体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An modularity-based overlapping community structure detecting algorithm
Many algorithms have been designed to detect community structure in social networks. However, most algorithms can only detect disjoint communities effectively. A new overlapping community structure detecting algorithm is proposed in this paper, which adopts modularity to community clustering. In order to evaluate the algorithm, Modularity by Newman and the NMI (Normalized Mutual Information) by Lancichinetti are used as the evaluation metrics. It is approved by the experiments that the proposed method works well to the real overlapping communities.
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