An modularity-based overlapping community structure detecting algorithm
Kui Meng, Gongshen Liu, Qiong Hu, Jianhua Li
Abstract
Kui Meng, Gongshen Liu, Qiong Hu, Jianhua Li
Abstract
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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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.
Key concepts: Modularity (biology), Clique percolation method, Disjoint sets, Community structure, Computer science, Cluster analysis, Data mining, Algorithm design