基于节点中心性和标签传播算法的社区检测

COMMUNITY DETECTION BASED ON NODE CENTRALITY AND LABEL PROPAGATION ALGORITHM

  • 摘要: 在复杂网络的探索过程中,关键节点的识别和社区结构的检测受到广泛关注,有助于人们更好地理解和利用复杂网络的结构特征,揭示节点之间的关系。提出库仑力中心性(CFC),并将其应用到标签传播算法(LPA)中设计了社团检测算法(CFCLPA),消除了LPA中的随机性,具有较高的社团结构识别能力。在真实网络和LFR基准网络下进行了一系列测试和比较,实验结果表明,该算法具有更优秀的社团检测性能。

     

    Abstract: In the process of exploring complex networks, the identification of critical nodes and the detection of community structure are widely concerned, which is helpful for people to better understand and use the structural characteristics of complex networks and reveal the relationships between nodes. In this paper, Coulomb force centrality (CFC) is proposed and applied to label propagation algorithm (LPA) to design community detection algorithm CFCLPA, which eliminates the randomness in LPA and has a high community structure detection performance. A series of tests and comparisons were made between real networks and LFR benchmark networks. The experimental results show that the proposed algorithm has better community detection performance.

     

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