摘要： 当前层次划分社团算法难以选取合适的初始节点，导致社团结构划分结果较差。为此，提出一种基于节点相异度的层次社团划分算法。给出度和接近度的评估标准，根据评估标准筛选网络的初始核心节点。为克服相异性指数在度量社团内节点相似度时的不足，引入节点的相异度评价准则，计算初始核心节点间的相似度，得到具有较高相似度的初始节点集。采用全局优化模块度的策略，从而实现对复杂网络的社团划分。应用于标准数据集的实验结果表明，与GN算法、FN算法相比，该算法划分效果更好，时间复杂度更低。

关键词: 复杂网络, 社团结构, 核心节点, 层次划分, 相异度, 模块度

Abstract: Aiming at the matter that the current level classification community algorithms are difficult to select the appropriate initial nodes lead to poor result for community structure divided, this paper proposes a layer partition algorithm based on hierarchical level to select core nodes. Algorithm screens the cores by evaluation criteria of degree and closeness. In order to overcome the shortage that dissimilarity index measures the similarity of nodes within a community, introducing node dissimilarity to evaluate the similarity between initial cores and the initial node set with higher node similarity. By adopting strategy of global optimization modularity and realizing community division in complex network, when applied in standard datasets, experimental results show that, compared with GN algorithm, FN algorithm, the proposed algorithm has a better classification effect and lower time complexity.

Key words: complex network, community structure, core node, hierarchical division, dissimilarity, modularity

中图分类号: 

• TP301.6