作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于粒子群优化的复杂网络社区挖掘

白 云,任国霞   

  1. (西北农林科技大学信息工程学院,西安712100)
  • 收稿日期:2014-04-08 出版日期:2015-03-15 发布日期:2015-03-13
  • 作者简介:白 云(1982 - ),女,硕士研究生,主研方向:数据挖掘,进化计算;任国霞(通讯作者),副教授。

Complex Network Community Mining Based on Particle Swarm Optimization

BAI Yun,REN Guoxia   

  1. (College of Information Engineering,Northwest A& F University,Xi’an 712100,China)
  • Received:2014-04-08 Online:2015-03-15 Published:2015-03-13

摘要: 为解决复杂网络社区结构挖掘的优化问题,根据复杂网络拓扑结构的先验知识,提出一种基于离散粒子群 优化的社区结构挖掘算法。将粒子的位置和速度定义在离散环境下,设计粒子的更新规则,在不需要事先指定社 区个数的前提下自动判断网络的最佳社区个数,给出局部搜索算子,该算子可以帮助算法跳出局部最优解,提高算 法的收敛速度和全局寻优能力。实验结果表明,与iMeme-net 算法相比,该算法能够准确地挖掘出复杂网络中隐藏 的社区结构,且执行速度较快。

关键词: 粒子群优化, 复杂网络, 社区结构, 社区挖掘, 局部搜索, 模块密度

Abstract: In order to solve the problem of community mining optimization from complex network,according to the prior knowledge of the topology structure of complex network,a complex network community mining algorithm based on Particle Swarm Optimization(PSO) is proposed. In the proposed algorithm,particle’s position and velocity are redefined in discrete case,particle’s update principles is redesigned,the proposed algorithm can automatically determine the best community numbers without knowing it in advance. In order to improve the global search ability of the proposed algorithm,a local search operator is designed,and this operator can help the algorithm to jump out of local optimum and improves the convergence speed. Experimental results demonstrate that the proposed algorithm can efficiently dig out the community structures hidden behind complex networks,and the execution speed is much faster than that of iMeme-net algorithm.

Key words: Particle Swarm Optimization (PSO), complex network, community structure, community mining, local search, modularity density

中图分类号: