Community detection in complex networks based on discrete quantum particle swarm

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (17): 45-46.

• 研究、探讨 • Previous Articles Next Articles

Community detection in complex networks based on discrete quantum particle swarm

CHEN Guoqiang1，ZHANG Xiguang2，ZHANG Xingang3

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

Received:1900-01-01 Revised:1900-01-01 Online:2011-06-11 Published:2011-06-11

应用离散量子粒子群的复杂网络社区检测

陈国强1，张西广2，张新刚3

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

Abstract

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.

Key words: complex networks, community detection, particle swarm optimization, modularity density

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

CHEN Guoqiang1，ZHANG Xiguang2，ZHANG Xingang3. Community detection in complex networks based on discrete quantum particle swarm[J]. Computer Engineering and Applications, 2011, 47(17): 45-46.

陈国强1，张西广2，张新刚3. 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.

[1]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[2]	ZHU Jiaying, GAO Maoting. AUV Path Planning Based on Particle Swarm Optimization and Improved Ant Colony Optimization [J]. Computer Engineering and Applications, 2021, 57(6): 267-273.
[3]	LI Jingxing, YANG Youlong. Feature Selection of Markov Blanket for High Dimensional Data [J]. Computer Engineering and Applications, 2021, 57(6): 58-66.
[4]	HU Xiaomin, WANG Mingfeng, ZHANG Shourong, LI Min. New Differential Evolution with Particle Swarm Optimization Algorithm for Text Clustering [J]. Computer Engineering and Applications, 2021, 57(4): 61-67.
[5]	WANG Jiayue, SHI Li. Research on Path Optimization and Charging Strategy of Electric Refrigerated Vehicle Considering Cost of Battery Capacity Degradation [J]. Computer Engineering and Applications, 2021, 57(24): 283-289.
[6]	CHEN Wanfen, WANG Yujia, LIN Weixing. Heterogeneous Ensemble Surrogate Assisted Multi-objective Particle Swarm Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 71-80.
[7]	DU Shouxin, WU Tao. Study on Application of Double Population Hybrid Genetic Algorithm in Cut Order Planning [J]. Computer Engineering and Applications, 2021, 57(22): 182-189.
[8]	LIN Weixing, WANG Yujia, CHEN Wanfen, LIANG Haina. High-Dimensional Data Feature Selection Algorithm Based on Multifactor Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(22): 199-207.
[9]	SHI Yuqiang, TIAN Yongzheng, ZHANG Yuqi, SHI Xiaoqiu. Solving FJSP by Multi-population Genetic Algorithms with Complex Network Structure [J]. Computer Engineering and Applications, 2021, 57(2): 257-266.
[10]	LI Leixiao, DENG Dan, LI Jie, WANG Yongsheng. All-to-All Comparison Computing Data Distribution Strategy Based on Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(15): 109-117.
[11]	YU Guolong, ZHAO Yong, WU Lian, CUI Zhongwei. Improvement of QPSO Algorithm and Its Application in DBN Parameter Optimization [J]. Computer Engineering and Applications, 2021, 57(10): 154-162.
[12]	LIAO Weilin, CHENG Shan, SHANG Dongdong, WEI Zhaobin. Particle Swarm Optimization Algorithm Integrated with Multiple-Strategies [J]. Computer Engineering and Applications, 2021, 57(1): 69-76.
[13]	WANG An, GU Yijun. Nodes Importance Ranking Method Based on Community Detection [J]. Computer Engineering and Applications, 2020, 56(8): 42-48.
[14]	ZHOU Rong, LI Jun, WANG Hao. Reverse Learning Particle Swarm Optimization Based on Grey Wolf Optimization [J]. Computer Engineering and Applications, 2020, 56(7): 48-56.
[15]	HUO Lin, LU Yinli. Improved Particle Swarm Optimization for Android Malware Detection [J]. Computer Engineering and Applications, 2020, 56(7): 96-101.

Community detection in complex networks based on discrete quantum particle swarm

应用离散量子粒子群的复杂网络社区检测

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics