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计算机工程 ›› 2020, Vol. 46 ›› Issue (7): 84-90,97. doi: 10.19678/j.issn.1000-3428.0055070

• 人工智能与模式识别 • 上一篇    下一篇

基于节点多属性相似性聚类的社团划分算法

邱少明1, 於涛1, 杜秀丽1, 陈波2   

  1. 1. 大连大学 通信与网络重点实验室, 辽宁 大连 116622;
    2. 岭南师范学院 信息工程学院, 广东 湛江 524048
  • 收稿日期:2019-05-30 修回日期:2019-07-23 发布日期:2019-08-09
  • 作者简介:邱少明(1977-),男,副教授,主研方向为智能故障诊断、复杂网络;於涛,硕士研究生;杜秀丽、陈波,教授、博士。
  • 基金资助:
    装备发展部预研基金(6140002010101,6140001030111)。

Community Division Algorithm Based on Similarity Clustering of Node Multiple Attribute

QIU Shaoming1, YU Tao1, DU Xiuli1, CHEN Bo2   

  1. 1. Key Laboratory of Communication and Network, Dalian University, Dalian, Liaoning 116622, China;
    2. School of Information Engineering, Lingnan Normal University, Zhanjiang, Guangdong 524048, China
  • Received:2019-05-30 Revised:2019-07-23 Published:2019-08-09

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。

关键词: 复杂网络, 社团划分, 节点属性, 相似度矩阵, 聚类

Abstract: 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.

Key words: complex network, community division, node attribute, similarity matrix, clustering

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