[1]朱大勇,侯晓荣,张新丽.遗传聚类的社团结构发现[J].智能系统学报,2009,4(01):81-84.
 ZHU Da-yong,HOU Xiao-rong,ZHANG Xin-Li.Discovery of community structure based on genetic clustering[J].CAAI Transactions on Intelligent Systems,2009,4(01):81-84.
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遗传聚类的社团结构发现(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第4卷
期数:
2009年01期
页码:
81-84
栏目:
出版日期:
2009-02-25

文章信息/Info

Title:
Discovery of community structure based on genetic clustering
文章编号:
1673-4785(2009)01-0081-04
作者:
朱大勇1侯晓荣2张新丽3
1.电子科技大学 计算机科学与工程学院,四川 成都 610054;2.电子科技大学 自动化工程学院,四川 成都 610054;3.成都信息工程学院 数学与信息科学系,四川 成都 610054
Author(s):
ZHU Da-yong1 HOU Xiao-rong2ZHANG Xin-Li3
1.College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054,China;2.College of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China;3.Department of Math and Information, Chengdu University of Information Technology, Chengdu 610054, China
关键词:
社团结构遗传聚类相异性指数模块度
Keywords:
community structuregenetic clusteringdissimilarity indexmodularity
分类号:
TP18
文献标志码:
A
摘要:
近年来在复杂网络中发现社团的结构引起了广泛的关注,目前已经提出了一些采用进化计算来分析复杂网络社团结构的方法.但大部分算法还存在处理过程复杂,空间复杂度过高等问题.通过确定网络节点的距离关系和聚类中心,提出一种新的基于遗传聚类的社团发现算法.将该算法用于真实网络的社团发现,实验结果验证了算法的可行性和有效性.
Abstract:
The discovery of community structure in complex networks has received widespread attention in recent years. Many methods based on evolutionary computation have been proposed to detect community structures in complex networks, but most of them are difficult to apply and have high degrees of spacecomplexity. In this paper we presented an algorithm for finding communities in complex networks using a genetic algorithm which examines distances between nodes and clustering centers. It was tested with real network datasets and the results of experiments demonstrated the feasibility of our algorithm.

参考文献/References:

[1]NEWMAN M E J. Fast algorithm for detecting community structure in networks [J]. Phys Rev E, 2004, 69(6) : 066133.
[2]GIRVAN M, NEWMAN M E J. Community structure in social and biological networks [J]. Proc Natl Acad Sci, 2001, 99: 7821-7826.
[3]TASGIN M, HERDAGDELEN A, BINGOL H. Community detection in complex networks using genetic algorithms [J/OL]. [2008-09-13].http://arxiv.org/abs/0711.0491.
[4]刘 婷, 胡宝清. 基于聚类分析的复杂网络中的社团探测[J]. 复杂系统与复杂性科学, 2007, 4(1): 28-35.
LIU Ting, HU Baoqing. Detecting community in complex networks using cluster analysis[J]. Complex Systems and Complexity Science, 2007, 4(1): 28-35.
[5]LIU Xin, LI Deyi, WANG Shuliang, et al. Effective algorithm for detecting community structure in complex networks based on GA and clustering [J].Lecture Notes in Computer Science,2007, 4488: 657-664.
[6]ZHOU Haijun. Distance, dissimilarity index and network community structure [J]. Phys Rev E, 2003, 67(6): 061901.
[7]NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks [J]. Phys Rev E, 2004, 69(2): 026113.

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备注/Memo

备注/Memo:
收稿日期:2008-11-07.
基金项目:国家自然科学基金资助项目(NSFC-10571095);国家973计划资助项目(NKBRPC-2004CB318003);成都信息工程学院自然科学与技术发展基金资助项目(CSRF200506).
 作者简介:
朱大勇,男,1975年生,讲师.主要研究方向为复杂网络、对等计算、分布式信息检索、软件工程.参加过多项科研项目,发表学术论文20余篇.
侯晓荣,男,1966年生,教授、博士生导师.主要研究方向为智能推理、机器证明.参加过多项科研项目,发表学术论文30余篇. 
张新丽,女,1973年生,副教授.主要研究方向为复杂系统、神经网络.参加过多项科研项目,发表学术论文10余篇.
更新日期/Last Update: 2009-03-24