IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v392y2013i5p1215-1231.html

Community detection based on modularity and an improved genetic algorithm

Author

Listed:
  • Shang, Ronghua
  • Bai, Jing
  • Jiao, Licheng
  • Jin, Chao

Abstract

Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.

Suggested Citation

  • Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:5:p:1215-1231
    DOI: 10.1016/j.physa.2012.11.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437112009673
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2012.11.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Wu, Jianshe & Wang, Xiaohua & Jiao, Licheng, 2012. "Synchronization on overlapping community network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 508-514.
    2. Zhang, Dawei & Xie, Fuding & Zhang, Yong & Dong, Fangyan & Hirota, Kaoru, 2010. "Fuzzy analysis of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5319-5327.
    3. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    4. Wang, Xiaohua & Jiao, Licheng & Wu, Jianshe, 2009. "Adjusting from disjoint to overlapping community detection of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(24), pages 5045-5056.
    5. Xie, Fuding & Ji, Min & Zhang, Yong & Huang, Dan, 2009. "The detection of community structure in network via an improved spectral method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(15), pages 3268-3272.
    6. Jiang, Jonathan Q. & McQuay, Lisa J., 2012. "Modularity functions maximization with nonnegative relaxation facilitates community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 854-865.
    7. Stoico, César O. & Renzi, Danilo G. & Vericat, Fernando, 2008. "A genetic algorithm for the 1D electron gas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 159-166.
    8. Wu, Zhihao & Lin, Youfang & Wan, Huaiyu & Tian, Shengfeng & Hu, Keyun, 2012. "Efficient overlapping community detection in huge real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2475-2490.
    9. Pan, Ying & Li, De-Hua & Liu, Jian-Guo & Liang, Jing-Zhang, 2010. "Detecting community structure in complex networks via node similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2849-2857.
    10. Faqeeh, Ali & Aghababaei Samani, Keivan, 2012. "Community detection based on the “clumpiness” matrix in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2463-2474.
    11. Wu, Jieyu & Shao, Xinyu & Li, Jinhang & Huang, Gang, 2012. "Scale-free properties of information flux networks in genetic algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1692-1701.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhao, Danling & Li, Jichao & Tan, Yuejin & Yang, Kewei & Ge, Bingfeng & Dou, Yajie, 2018. "Optimization adjustment of human resources based on dynamic heterogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 45-57.
    2. Bilal, Saoud & Abdelouahab, Moussaoui, 2017. "Evolutionary algorithm and modularity for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 89-96.
    3. Fu, Yu-Hsiang & Huang, Chung-Yuan & Sun, Chuen-Tsai, 2016. "Using a two-phase evolutionary framework to select multiple network spreaders based on community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 840-853.
    4. Li, Yafang & Jia, Caiyan & Li, Jianqiang & Wang, Xiaoyang & Yu, Jian, 2018. "Enhanced semi-supervised community detection with active node and link selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 219-232.
    5. Leila M Naeni & Hugh Craig & Regina Berretta & Pablo Moscato, 2016. "A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-27, August.
    6. Guobin Chen & Tangsen Huang, 2019. "Community privacy estimation method based on key node method in space social Internet of Things," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    7. Chen, Kaiqi & Bi, Weihong, 2019. "A new genetic algorithm for community detection using matrix representation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    8. Moradi, Mehdi & Parsa, Saeed, 2019. "An evolutionary method for community detection using a novel local search strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 457-475.
    9. Cai, Biao & Wang, Yanpeng & Zeng, Lina & Hu, Yanmei & Li, Hongjun, 2020. "Edge classification based on Convolutional Neural Networks for community detection in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    10. He, Zhipeng & Zhang, Shuguang & Hu, Jun & Dai, Fei, 2024. "An adaptive time series segmentation algorithm based on visibility graph and particle swarm optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    11. Saoud, Bilal & Moussaoui, Abdelouahab, 2018. "A new hierarchical method to find community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 418-426.
    12. Li, Wei & Huang, Ce & Wang, Miao & Chen, Xi, 2017. "Stepping community detection algorithm based on label propagation and similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 145-155.
    13. Shang, Ronghua & Liu, Huan & Jiao, Licheng, 2017. "Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1-24.
    14. Li, Shudong & Jiang, Laiyuan & Wu, Xiaobo & Han, Weihong & Zhao, Dawei & Wang, Zhen, 2021. "A weighted network community detection algorithm based on deep learning," Applied Mathematics and Computation, Elsevier, vol. 401(C).
    15. Peng Wu & Li Pan, 2015. "Multi-Objective Community Detection Based on Memetic Algorithm," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-31, May.
    16. Dhuha Abdulhadi Abduljabbar & Siti Zaiton Mohd Hashim & Roselina Sallehuddin, 2020. "Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(2), pages 225-252, June.
    17. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
    18. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.
    19. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
    2. Wu, Jianshe & Lu, Rui & Jiao, Licheng & Liu, Fang & Yu, Xin & Wang, Da & Sun, Bo, 2013. "Phase transition model for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1287-1301.
    3. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    4. Yang, Kai & Guo, Qiang & Liu, Jian-Guo, 2018. "Community detection via measuring the strength between nodes for dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 256-264.
    5. Liu, Xu & Forrest, Jeffrey Yi-Lin & Luo, Qiang & Yi, Dong-Yun, 2012. "Detecting community structure using biased random merging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1797-1810.
    6. Ji, Junzhong & Song, Xiangjing & Liu, Chunnian & Zhang, Xiuzhen, 2013. "Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3260-3272.
    7. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.
    8. Mu, Caihong & Liu, Yong & Liu, Yi & Wu, Jianshe & Jiao, Licheng, 2014. "Two-stage algorithm using influence coefficient for detecting the hierarchical, non-overlapping and overlapping community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 47-61.
    9. Shang, Ronghua & Liu, Huan & Jiao, Licheng, 2017. "Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1-24.
    10. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.
    11. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    12. Jianlin Zhou & An Zeng & Ying Fan & Zengru Di, 2018. "Identifying important scholars via directed scientific collaboration networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1327-1343, March.
    13. Yuan, Peiyan & Tang, Shaojie, 2015. "Community-based immunization in opportunistic social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 85-97.
    14. Guo, Wei-Feng & Zhang, Shao-Wu, 2016. "A general method of community detection by identifying community centers with affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 508-519.
    15. Gholami, Maryam & Sheikhahmadi, Amir & Khamforoosh, Keyhan & Jalili, Mahdi, 2022. "Overlapping community detection in networks based on Neutrosophic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    16. Ni, Shunjiang & Weng, Wenguo & Zhang, Hui, 2011. "Modeling the effects of social impact on epidemic spreading in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4528-4534.
    17. Xiao‐Bing Hu & Hang Li & XiaoMei Guo & Pieter H. A. J. M. van Gelder & Peijun Shi, 2019. "Spatial Vulnerability of Network Systems under Spatially Local Hazards," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 162-179, January.
    18. Lou, Hao & Li, Shenghong & Zhao, Yuxin, 2013. "Detecting community structure using label propagation with weighted coherent neighborhood propinquity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3095-3105.
    19. Weijiang Li & Hiroyuki Kurata, 2008. "Visualizing Global Properties of Large Complex Networks," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-4, July.
    20. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Overlapping community detection using neighborhood ratio matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 510-521.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:392:y:2013:i:5:p:1215-1231. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.