title: Memetic algorithm for community detection in networks.
abstract: Community structure is one of the most important properties in networks, and community detection has received an enormous amount of attention in recent years. Modularity is by far the most used and best known quality function for measuring the quality of a partition of a network, and many community detection algorithms are developed to optimize it. However, there is a resolution limit problem in modularity optimization methods. In this study, a memetic algorithm, named Meme-Net, is proposed to optimize another quality function, modularity density, which includes a tunable parameter that allows one to explore the network at different resolutions. Our proposed algorithm is a synergy of a genetic algorithm with a hill-climbing strategy as the local search procedure. Experiments on computer-generated and real-world networks show the effectiveness and the multiresolution ability of the proposed method.
authors.affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi Province, China.
authors.forename: Maoguo
authors.initials: M
authors.lastname: Gong
authors.forename: Bao
authors.initials: B
authors.lastname: Fu
authors.forename: Licheng
authors.initials: L
authors.lastname: Jiao
authors.forename: Haifeng
authors.initials: H
authors.lastname: Du
mesh_terms: D000465:Algorithms; D000818:Animals; D019058:Community Networks; D003199:Computer Systems; D003201:Computers; D004289:Dolphins; D005538:Football; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D011057:Politics