IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v503y2018icp410-423.html
   My bibliography  Save this article

A link clustering based memetic algorithm for overlapping community detection

Author

Listed:
  • Li, Mingming
  • Liu, Jing

Abstract

Community detection has attracted plenty of attention in the field of complex networks recently, since communities often play important roles in networked systems. Overlapping communities are one of the characteristics of social networks, describing the phenomenon that a node may belong to more than one social group. Thus, it is necessary to find overlapping community structures for realistic social network analyses. In this paper, we propose a link clustering based memetic algorithm for detecting overlapping communities. Since links usually represent the unique relationships among nodes, link clustering can find link groups with the same characteristics. As a result, nodes are naturally partitioned into multiple communities. The proposed algorithm optimizes a modularity density function which is able to identify densely connected groups of links on the weighted line graph modeling the network, and then maps link communities to node communities based on a novel genotype representation. In our method, the number of communities can be automatically determined. Experimental results on general and sparse networks show that our method can successfully detect overlapping community structures and almost all the overlapping nodes.

Suggested Citation

  • Li, Mingming & Liu, Jing, 2018. "A link clustering based memetic algorithm for overlapping community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 410-423.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:410-423
    DOI: 10.1016/j.physa.2018.02.133
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711830253X
    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.2018.02.133?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    2. 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.
    3. Stephen Kelley & Mark Goldberg & Malik Magdon-Ismail & Konstantin Mertsalov & Al Wallace, 2012. "Defining and Discovering Communities in Social Networks," Springer Optimization and Its Applications, in: My T. Thai & Panos M. Pardalos (ed.), Handbook of Optimization in Complex Networks, edition 1, chapter 0, pages 139-168, Springer.
    4. Li, Zhangtao & Liu, Jing, 2016. "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 336-347.
    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. Kong, Hanzhang & Kang, Qinma & Li, Wenquan & Liu, Chao & Kang, Yunfan & He, Hong, 2019. "A hybrid iterated carousel greedy algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).

    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. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    2. Shang, Jiaxing & Liu, Lianchen & Li, Xin & Xie, Feng & Wu, Cheng, 2016. "Targeted revision: A learning-based approach for incremental community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 70-85.
    3. Ying Song & Zhiwen Zheng & Yunmei Shi & Bo Wang, 2023. "GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks," Mathematics, MDPI, vol. 11(15), pages 1-16, July.
    4. Zhang, Zhiwei & Wang, Zhenyu, 2015. "Mining overlapping and hierarchical communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 25-33.
    5. Masa Tsuchiya & Vincent Piras & Alessandro Giuliani & Masaru Tomita & Kumar Selvarajoo, 2010. "Collective Dynamics of Specific Gene Ensembles Crucial for Neutrophil Differentiation: The Existence of Genome Vehicles Revealed," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-10, August.
    6. 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.
    7. Nie, Yanyi & Li, Wenyao & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Markovian approach to tackle competing pathogens in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    8. Rizman Žalik, Krista & Žalik, Borut, 2014. "A local multiresolution algorithm for detecting communities of unbalanced structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 380-393.
    9. Zhang, Shihua & Wang, Rui-Sheng & Zhang, Xiang-Sun, 2007. "Identification of overlapping community structure in complex networks using fuzzy c-means clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 483-490.
    10. Chen, Lei & Kou, Yingxin & Li, Zhanwu & Xu, An & Wu, Cheng, 2018. "Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 754-773.
    11. Giorgio Gronchi & Marco Raglianti & Fabio Giovannelli, 2021. "Network Theory and Switching Behaviors: A User Guide for Analyzing Electronic Records Databases," Future Internet, MDPI, vol. 13(9), pages 1-12, August.
    12. Amulyashree Sridhar & Sharvani GS & AH Manjunatha Reddy & Biplab Bhattacharjee & Kalyan Nagaraj, 2019. "The Eminence of Co-Expressed Ties in Schizophrenia Network Communities," Data, MDPI, vol. 4(4), pages 1-23, November.
    13. Shen Wang & Jun Wu & Yutao Zhang, 2018. "Consumer preference–enabled intelligent energy management for smart cities using game theoretic social tie," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    14. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    15. Jiang, Yawen & Jia, Caiyan & Yu, Jian, 2013. "An efficient community detection method based on rank centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2182-2194.
    16. 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.
    17. Selen Onel & Abe Zeid & Sagar Kamarthi, 2011. "The structure and analysis of nanotechnology co-author and citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 119-138, October.
    18. Rawya Zreik & Pierre Latouche & Charles Bouveyron, 2017. "The dynamic random subgraph model for the clustering of evolving networks," Computational Statistics, Springer, vol. 32(2), pages 501-533, June.
    19. Pollner, Péter & Palla, Gergely & Vicsek, Tamás, 2010. "Clustering of tag-induced subgraphs in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5887-5894.
    20. Badie, Reza & Aleahmad, Abolfazl & Asadpour, Masoud & Rahgozar, Maseud, 2013. "An efficient agent-based algorithm for overlapping community detection using nodes’ closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5231-5247.

    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:503:y:2018:i:c:p:410-423. 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.