摘要
针对当前社团划分算法存在划分方式单一和划分结果准确度低等问题,提出一种基于节点多属性相似性聚类的社团划分算法SM-CD。根据社会网络特性定义网络节点的结构属性与自身属性,通过调整两类属性在网络中所占的权重计算网络节点之间的相似度矩阵,并将网络节点按照相似度和模块度指标划分为不同的社团。在Zachary和Football真实网络数据集上的实验结果表明,SM-CD算法相比Newman、GN等算法具有更高的社团划分准确率。
Existing community division algorithms lack diversity in the division method,and division results are not accurate.To address the problem,this paper proposes a community division algorithm,SM-CD,on the basis of similarity clustering of multiple attributes of nodes.The algorithm uses social network features to define the structure attributes of nodes and the attributes of oneself.By adjusting the weight of two kinds of attributes in network,the similarity matrix of network nodes is calculated.Then the nodes are divided into different communities according to similarity and modularity.Experimental results on the real network data from Zachary and Football show that SM-CD has a higher accuracy rate in community division than Newman,GN and other algorithms.
作者
邱少明
於涛
杜秀丽
陈波
QIU Shaoming;YU Tao;DU Xiuli;CHEN Bo(Key Laboratory of Communication and Network,Dalian University,Dalian,Liaoning 116622,China;School of Information Engineering,Lingnan Normal University,Zhanjiang,Guangdong 524048,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第7期84-90,97,共8页
Computer Engineering
基金
装备发展部预研基金(6140002010101,6140001030111)。
关键词
复杂网络
社团划分
节点属性
相似度矩阵
聚类
complex network
community division
node attribute
similarity matrix
clustering
