[1]孙圣波,朱保平,杨晓光.基于三角模体的社团发现算法[J].南京理工大学学报(自然科学版),2017,41(01):35.[doi:10.14177/j.cnki.32-1397n.2017.41.01.005]
 Sun Shengbo,Zhu Baoping,Yang Xiaoguang.Community discovery algorithm based on triangular motifs[J].Journal of Nanjing University of Science and Technology,2017,41(01):35.[doi:10.14177/j.cnki.32-1397n.2017.41.01.005]
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基于三角模体的社团发现算法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年01期
页码:
35
栏目:
出版日期:
2017-02-28

文章信息/Info

Title:
Community discovery algorithm based on triangular motifs
文章编号:
1005-9830(2017)01-0035-06
作者:
孙圣波朱保平杨晓光
南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Sun ShengboZhu BaopingYang Xiaoguang
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
三角模体 社团发现 期望极大算法 混合隶属度 链接关系
Keywords:
triangular motifs community discovery expectation-maximization algorithm mixed membership degree link relationship
分类号:
TP311
DOI:
10.14177/j.cnki.32-1397n.2017.41.01.005
摘要:
为了提高社团发现算法的效率,提出了一种基于三角模体和期望极大的社团结构发现(Community structure discovery based on triangular motifs and expectation-maximization,CSDTME)模型的社团发现算法。CSDTME模型采用三角模体对网络进行表示,考虑了节点的混合隶属度及社团间的链接关系,用期望极大算法计算模型涉及的参数,采用全三角模体和两边三角模体作为计算对象,通过减少计算对象来提高算法的效率,根据参数结果可得到节点的社团隶属度及社团间的链接关系。实验结果表明:在保证社团发现能力的同时,该算法能够提高社团发现的效率。
Abstract:
In order to improve the efficiency of community detection algorithm,this paper proposes a community structure discovery based on the triangular motifs and expectation-maximization models of a community discovery algorithm.The model based on the triangle motif represents the network,considering the links between nodes and mixed membership between communities.The expectation maximization algorithm is used to solve the parameters of the model,triangle motif and bilateral triangular norm body as an object of calculation by reducing the calculation object to improve the efficiency of the algorithm.The results are obtained according to the parameters of node membership links and associations between communities.The experimental results show that the algorithm can improve the efficiency of the community discovery and ensure the capacity of the community discovery.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-08-01 修回日期:2016-10-11
作者简介:孙圣波(1991-),男,硕士生,主要研究方向:社团发现,E-mail:sunshine11ball@163.com; 通讯作者:朱保平(1964),男,副教授,主要研究方向:密码学,信息安全,E-mail:zbp2068@126.com。
引文格式:孙圣波,朱保平,杨晓光.基于三角模体的社团发现算法[J].南京理工大学学报,2017,41(1):35-40.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-02-28