A Multiobjective Genetic Algorithm to Find Communities in Complex Networks

被引：268

作者：

Pizzuti, Clara ^{[1
]}

机构：

[1] Natl Res Council Italy, Inst High Performance Comp & Networking, I-87036 Cosenza, Italy

来源：

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2012年 / 16卷 / 03期

关键词：

Complex networks; multiobjective clustering; multiobjective evolutionary algorithms; EVOLUTIONARY ALGORITHMS; MODULARITY;

D O I：

10.1109/TEVC.2011.2161090

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A multiobjective genetic algorithm to uncover community structure in complex network is proposed. The algorithm optimizes two objective functions able to identify densely connected groups of nodes having sparse inter-connections. The method generates a set of network divisions at different hierarchical levels in which solutions at deeper levels, consisting of a higher number of modules, are contained in solutions having a lower number of communities. The number of modules is automatically determined by the better tradeoff values of the objective functions. Experiments on synthetic and real life networks show that the algorithm successfully detects the network structure and it is competitive with state-of-the-art approaches.

引用

页码：418 / 430

页数：13