复杂网络中的社团结构算法综述  

Overview of Algorithms for Detecting Community Structure in Complex Networks

作　　者：汪小帆[1] 刘亚冰[1] 

机构地区：[1]上海交通大学电子信息与电气工程学院,上海闵行区200240

出　　处：《电子科技大学学报》2009年第5期537-543,共7页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(60674045;60731160629);上海市优秀学科带头人计划(07XD14017)

摘　　要：社团结构是复杂网络的一个极其重要的特性,网络社团结构挖掘在生物学、计算机科学和社会学等多个领域都具有很重要的意义。近年来,针对不同类型的大规模复杂网络,人们提出了很多寻找社团结构的算法。该文综述了该领域最新的比较有代表性的一些算法,重点分析了基于模块度指标的改进算法,能够体现社团层次性和重叠性的新算法,衡量社团划分算法好坏的基准图。最后展望了该领域的未来研究方向。Community structure is a very important property of complex networks. Detecting communities in networks is of great importance in biology, computer science, sociology and so on. In recent years, a lot of community discovery algorithms have been proposed aiming at different kinds of large scale complex networks. In this paper, we review some latest representative algorithms, focusing on the improved methods based on the modularity function, the algorithms which can detect overlapping and hierarchical community structure in networks, and the benchmark in detecting communities. Finally, some future directions are pointed out.

关 键 词：复杂网络 社团结构 层次性 模块度函数 重叠性 

分 类 号：N941[自然科学总论—系统科学] TP311.13[自动化与计算机技术—计算机软件与理论]