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中文摘要：
社团结构对复杂系统的结构特性和动力学特性有重要影响. 提出了一个度量社团相似度的模型, 称为簇相似度. 该模型能够度量两个社团的相似度大小, 为研究社团间的作用机制提供帮助. 而且基于该模型, 设计了一个社团划分算法. 算法采用层次聚类的思想, 每次合并两个相似度最大的社团, 并通过一个评价函数选择最优社团划分. 数值实验以及与CNM, GN, EigenMod等主流算法做比较, 表明本算法的精度和效率都比较高, 尤其对于边密度较高的网络, 性能非常理想.
英文摘要：
Community structure has an important influence on the structural and dynamic characteristics of the complex system. In the present study, a group similarity model is proposed for the measurement of similarity between two communities. So it can help us understand the mechanism of inter action between these communities. Moreover, based on this model, a hierarchical clustering based algorithm for network community structure detection is put forward. By this algorithm, one pair of communities with the largest similarity is merged in each iteration. And then an evaluation function is adopted for choosing the optimal partition. The algorithm gives a higher performance than many state-of-the-art community detection algorithms when tested on a series of real-world and synthetic networks. Especially, it performs better when the edge density of the network is high.
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