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| 应用离散量子粒子群的复杂网络社区检测 | |
| 引用本文： | 陈国强,张西广,张新刚.应用离散量子粒子群的复杂网络社区检测[J].计算机工程与应用,2011,47(17):45-46. |
| 作者姓名： | 陈国强 张西广 张新刚 |
| 作者单位： | 1. 河南大学计算机与信息工程学院,河南开封,475004 2. 中原工学院计算机学院,郑州,450007 3. 南阳师范学院计算机与信息技术学院,河南南阳,473061 |
| 基金项目： | 国家自然科学基金，河南省重点科技攻关项目，河南省教育厅自然科学研究项目 |
| 摘 要： | 针对模块度存在的解限制问题,分析了复杂网络社区检测中一种新的测度模块密度。采用二分策略,通过最大化模块密度,提出了基于离散量子粒子群优化进行复杂网络社区检测的算法。通过人工网络和现实网络的实验表明,算法具有较高的检测性能,并且在网络越来越模糊时,也能够检测出网络社区结构。 |
| 关 键 词： | 复杂网络 社区检测 粒子群优化 模块密度 |
| 修稿时间： | |
Community detection in complex networks based on discrete quantum particle swarm |
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| CHEN Guoqiang,ZHANG Xiguang,ZHANG Xingang.Community detection in complex networks based on discrete quantum particle swarm[J].Computer Engineering and Applications,2011,47(17):45-46. | |
| Authors: | CHEN Guoqiang ZHANG Xiguang ZHANG Xingang |
| Affiliation: | 1.School of Computer and Information Engineering,Henan University,Kaifeng,Henan 475004,China 2.School of Computer,Zhongyuan Institute of Technology,Zhengzhou 450007,China 3.School of Computer and Information Technology,Nanyang Normal University,Nanyang,Henan 473061,China |
| Abstract: | To overcome the resolution limits drawback of modularity function, a new measure of modularity density in complex network community detection is studied.With bi-partitioning strategy,by maximizing the module density,an algorithm is proposed based on discrete quantum particle swarm optimization for complex network community detection.Through the artificial network and real network experiments it is showed that this algorithm has high detection performance.And when the network becomes increasingly blurred,it can detect the network community structure well. |
| Keywords: | complex networks community detection particle swarm optimization modularity density |
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