基于邻居节点相异性的社团发现新算法  

A New Algorithm for Community Detecting Based on Neighbor Node Dissimilarity

作　　者：张思源 覃森[1] 张智丰[1] ZHANG Siyuan;QIN Sen;ZHANG Zhifeng(School of Science,Hangzhou Dianzi University,Hangzhou Zhejiang 310018,China)

机构地区：[1]杭州电子科技大学理学院,浙江杭州310018

出　　处：《杭州电子科技大学学报（自然科学版）》2018年第5期98-102,共5页Journal of Hangzhou Dianzi University：Natural Sciences

摘　　要：基于网络节点的邻居节点相异性和朴素的划分思想,提出了一种社团发现新算法。算法通过循环迭代移除网络中相异性最高的边获得新的社团结构,同时计算出社团结构的模块度。进而,寻求划分后网络模块度的最大值,得到社团结构的划分结果与最优社团数量。对计算机模拟网络和一些真实网络进行社团发现,结果表明,该算法能够有效地发现复杂网络的社团结构,且具有较高的模块度。Based on neighbor node dissimilarity and simple division of network nodes,a new community detecting algorithm is proposed.The algorithm obtains the new community structure by loop iteration to remove the edge of highest dissimilarity in the network.At the same time,the modularity of the community structure is calculated.Then,the maximum value of the network modularity is obtained to indicate the division result of the community structure and the number of the optimal community.Numerical results of the community detecting of computer simulation network and some real networks show that,the proposed algorithm can find the community structure of complex network quickly and effectively.Moreover,the detected community structure has high modularity.

关 键 词：社团发现算法 相异性 模块度 

分 类 号：O157.5[理学—数学]