复杂网络中的社团结构算法综述

Overview of Algorithms for Detecting Community Structure in Complex Networks

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.