基于多层节点相似度的社区发现方法

摘要

社区发现是复杂网络研究中的一项重要研究内容,基于节点相似度的凝聚方法是一种典型的社区发现方法.针对现有节点相似度计算方法中存在的不足,提出一种基于多层节点的节点相似度计算方法,该方法既可以有效地计算节点之间的相似度,又可以解决节点相似度相同时的节点合并选择问题.进一步基于这种改进的节点相似度计算方法和团体之间的连接紧密度度量准则构建社区发现模型,并在真实世界的网络上进行社区发现实验.与GN算法、Fast Newman算法和改进的标签传播算法的实验结果相比,该模型可以更加准确地找到各个社区的成员.%Community detection is an important research content in complex network,and the agglomerative method based on the node similarity is a typical method of community detection.Aiming at the shortages of the existing method for calculating the node similarity,this paper proposed a novel method based on the multi-layer node similarity,which can not only calculate the similarity between nodes more efficiently,but also solve the problem of merging nodes when the node similarity is same.Furthermore,this paper constructed the community detection model based on the improved calculation method of the node similarity and the measure criteria of connection tightness between groups,and conducted the community detection experiments in real world network.Compared with the experimental results of GN algorithm,Fast Newman algorithm and the improved label propagation algorithm,the proposed model can be more accurate to find the members of each community.