基于改进的ACCA的复杂网络社团结构发现  

Community Structure Detection in Complex Networks Based on Improved ACCA

作　　者：臧丽[1] 王红[1] 杨通辉[1] 

机构地区：[1]山东师范大学信息科学与工程学院,山东济南250014

出　　处：《计算机技术与发展》2012年第10期129-132,共4页Computer Technology and Development

基　　金：国家自然科学基金资助项目(60970004);山东省研究生教育创新计划项目(SDYY10059);山东师范大学研究生重点课程项目

摘　　要：复杂网络社团结构划分日益成为近年来复杂网络的研究热点,到目前为止,已经提出了很多分析复杂网络社团结构的算法。该文在聚类算法的基础上,提出了一种基于改进的ACCA的复杂网络社团结构发现方法。该文提出的方法的好处是社团数目不用事先被指定,并且此算法最大的优点就是能获取全局最优解。通过ZacharyKarate Club经典模型验证了该算法的可行性和有效性,实验结果表明,该算法能成功地发现各个社团,是一种行之有效的网络社团发现算法。Community structure identification has been one of the most popular research areas in recent years ：rod there has been many algorithm proposed so far to detect community structures in complex networks. In this paper, an algorithm for detecting community struttares in complex network is presented, which is based on the improved ant colony clustering algorithm, based on the clustering algorithm. The benefit of the method proposed in this paper is number of community does not need to specified,and the biggest advantage of this al- gorithm is that it can obtain the global optimal solution. The feasibility and effectiveness of the algorithm have been validated through the ZacharyKarate Club classical model, experimental results show that the algorithm can successfully find each community,is a kind of effective algorithm to find network community.

关 键 词：复杂网络 社团结构 聚类算法 改进的蚁群聚类算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]