复杂网络局部结构的涌现:共同邻居驱动网络演化

在对真实网络的小世界和无标度特性进行了大量深入考量之后,最近的研究热点开始转移到更加细致的局部结构.实证数据显示,大量真实网络具有幂律的低阶集团度分布.这一普适的规律,无法由富者愈富以及熟人推荐的网络生长机理再现.本文提出一种由共同邻居驱动的网络演化模型,该模型能够重现实证研究所观察到的幂律集团度分布,暗示共同邻居驱动是复杂网络局部结构涌现形成的内在机理. 关键词： 复杂网络 演化模型 集团度分布 共同邻居     

Detecting Overlapping Communities Based on Community Cores in Complex Networks

The identification of communities is significant for the understanding of network structures and functions. Since some nodes naturally belong to several communities, the study of overlapping community structures has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. We propose a new algorithm. The main idea is first to find the core of a community by detecting maximal cliques and then merging some tight community cores to form the community. Experimental results on two real networks demonstrate that the present algorithm is more accurate for detecting overlapping community structures, compared with some well-known results and methods.