IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v407y2014icp7-14.html
   My bibliography  Save this article

Uncovering overlapping community structures by the key bi-community and intimate degree in bipartite networks

Author

Listed:
  • Cui, Yaozu
  • Wang, Xingyuan

Abstract

Although many successful algorithms have been designed to discover community structures in network, most of them are dedicated to disjoint and non-overlapping communities. Very few of them are intended to discover overlapping communities, particularly the detection of such communities have hardly been explored in the bipartite networks. In this paper, a novel algorithm is proposed to detect overlapping community structures in bipartite networks. After analyzing the topological properties in bipartite networks, the key bi-communities and free-nodes are introduced. Firstly, some key bi-communities and free-nodes are extracted from the original bipartite networks. Then the free-nodes are allocated into a certain key bi-community by some given rules. In addition, the proposed algorithm successfully finds overlapping vertices between communities. Experimental results using some real-world networks data show that the performance of the proposed algorithm is satisfactory.

Suggested Citation

  • Cui, Yaozu & Wang, Xingyuan, 2014. "Uncovering overlapping community structures by the key bi-community and intimate degree in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 7-14.
  • Handle: RePEc:eee:phsmap:v:407:y:2014:i:c:p:7-14
    DOI: 10.1016/j.physa.2014.03.077
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711400288X
    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.2014.03.077?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. Zhang, Peng & Wang, Jinliang & Li, Xiaojia & Li, Menghui & Di, Zengru & Fan, Ying, 2008. "Clustering coefficient and community structure of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6869-6875.
    2. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    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. Wang, Xingyuan & Qin, Xiaomeng, 2016. "Asymmetric intimacy and algorithm for detecting communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 569-578.
    2. Yubo Peng & Bofeng Zhang & Furong Chang, 2021. "Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density," Future Internet, MDPI, vol. 13(4), pages 1-21, March.
    3. Nan, Dong-Yang & Yu, Wei & Liu, Xiao & Zhang, Yun-Peng & Dai, Wei-Di, 2018. "A framework of community detection based on individual labels in attribute networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 523-536.
    4. Wang, Tao & Wang, Hongjue & Wang, Xiaoxia, 2015. "A novel cosine distance for detecting communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 21-35.
    5. Luo, Chao & Zhang, Xiaolin & Liu, Hong & Shao, Rui, 2016. "Cooperation in memory-based prisoner’s dilemma game on interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 560-569.
    6. Hu, Fang & Liu, Yuhua, 2016. "A new algorithm CNM-Centrality of detecting communities based on node centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 138-151.
    7. Zhang, Hong, 2015. "Moderate tolerance promotes tag-mediated cooperation in spatial Prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 52-61.
    8. 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.
    9. 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.
    10. Li, Jin-Yang & Teng, Jing & Wang, Hui, 2023. "Integrating bipartite network modelling and overlapping community detection: A new method to evaluate transit line coordination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(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. Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.
    2. Zhang, Yun & Liu, Yongguo & Li, Jieting & Zhu, Jiajing & Yang, Changhong & Yang, Wen & Wen, Chuanbiao, 2020. "WOCDA: A whale optimization based community detection algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    3. Rezvanian, Alireza & Meybodi, Mohammad Reza, 2015. "Sampling social networks using shortest paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 254-268.
    4. Liu, X. & Murata, T., 2010. "Advanced modularity-specialized label propagation algorithm for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1493-1500.
    5. Etienne Côme & Nicolas Jouvin & Pierre Latouche & Charles Bouveyron, 2021. "Hierarchical clustering with discrete latent variable models and the integrated classification likelihood," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 957-986, December.
    6. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    7. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    8. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    9. Hu, Fang & Liu, Jia & Li, Liuhuan & Liang, Jun, 2020. "Community detection in complex networks using Node2vec with spectral clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    10. Xu, Shuang & Wang, Pei, 2017. "Identifying important nodes by adaptive LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 654-664.
    11. Rosenberg, Eric, 2018. "Generalized Hausdorff dimensions of a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 1-17.
    12. Antiqueira, L. & Nunes, M.G.V. & Oliveira Jr., O.N. & F. Costa, L. da, 2007. "Strong correlations between text quality and complex networks features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 811-820.
    13. Fessina, Massimiliano & Zaccaria, Andrea & Cimini, Giulio & Squartini, Tiziano, 2024. "Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    14. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    15. Su, Zhen & Liu, Fanzhen & Gao, Chao & Gao, Shupeng & Li, Xianghua, 2018. "Inferring infection rate based on observations in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 170-176.
    16. Šubelj, Lovro & Bajec, Marko, 2011. "Community structure of complex software systems: Analysis and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2968-2975.
    17. Li, Xiaojia & Li, Menghui & Hu, Yanqing & Di, Zengru & Fan, Ying, 2010. "Detecting community structure from coherent oscillation of excitable systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 164-170.
    18. Zhang, Dayong & Men, Hao & Zhang, Zhaoxin, 2024. "Assessing the stability of collaboration networks: A structural cohesion analysis perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    19. Yazdanparast, Sakineh & Havens, Timothy C., 2017. "Modularity maximization using completely positive programming," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 20-32.
    20. Sun, Peng Gang & Che, Wanping & Quan, Yining & Wang, Shuzhen & Miao, Qiguang, 2022. "Random networks are heterogeneous exhibiting a multi-scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).

    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:407:y:2014:i:c:p:7-14. 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.