中南大学学报（自然科学版）2018，Vol.49Issue(1)：109-117,9.DOI:10.11817/j.issn.1672-7207.2018.01.015
自适应进化蝙蝠算法下的复杂网络社区发现
Research on community detection in complex networks based on self-adaptive evolution bat algorithm
摘要
Abstract
To solve the problem of low accuracy and slow convergence speed in the community detection of the complex networks, an improved bat algorithm called self-adaptive evolution bat algorithm (SEBA) was proposed by combining the idea of location update and speed update which exists in genetic algorithm. Firstly, network modularity Q was employed as objective function and label propagation method was applied to initialize the population based on the character encoding; then the speed of bat individuals is turned into mutation probability and crossover operator was used to update location information to achieve the self-adaptive evolutionary of bat. Finally, the proposed SEBA was tested on both benchmark networks and real networks in order to compare with other competitive community detection algorithms. The results show that the proposed algorithm significantly accelerates the convergence speed and increases accuracy in the presence of large-scale network structure.关键词
复杂网络/社区发现/模块度/蝙蝠算法/自适应进化Key words
complex network/community detection/modularity/bat algorithm/self-adaptive/evolution分类
信息技术与安全科学引用本文复制引用
唐朝伟,李彦,段青言,杨险峰,胡佩,陈冠豪..自适应进化蝙蝠算法下的复杂网络社区发现[J].中南大学学报（自然科学版）,2018,49(1):109-117,9.基金项目
重庆市科委社会民生专项(cstc2013shmszx0500) （cstc2013shmszx0500）
重庆市教委科学技术研究项目(KJ1729405) （KJ1729405）
佛山市经济科技发展专项(2015) (Project(cstc2013shmszx0500) supported by a research grant from Chongqing Science & Technology Commission （2015）
Project(KJ1729405) supported by a Scientific and Technological Research Program from Chongqing Municipal Education Commission （KJ1729405）
Project(2015) supported by Foshan Economic and Information Bureau) （2015）
