IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0038704.html
   My bibliography  Save this article

Discovering Communities through Friendship

Author

Listed:
  • Greg Morrison
  • L Mahadevan

Abstract

We introduce a new method for detecting communities of arbitrary size in an undirected weighted network. Our approach is based on tracing the path of closest‐friendship between nodes in the network using the recently proposed Generalized Erds Numbers. This method does not require the choice of any arbitrary parameters or null models, and does not suffer from a system‐size resolution limit. Our closest‐friend community detection is able to accurately reconstruct the true network structure for a large number of real world and artificial benchmarks, and can be adapted to study the multi‐level structure of hierarchical communities as well. We also use the closeness between nodes to develop a degree of robustness for each node, which can assess how robustly that node is assigned to its community. To test the efficacy of these methods, we deploy them on a variety of well known benchmarks, a hierarchal structured artificial benchmark with a known community and robustness structure, as well as real‐world networks of coauthorships between the faculty at a major university and the network of citations of articles published in Physical Review. In all cases, microcommunities, hierarchy of the communities, and variable node robustness are all observed, providing insights into the structure of the network.

Suggested Citation

  • Greg Morrison & L Mahadevan, 2012. "Discovering Communities through Friendship," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    • Handle: RePEc:plo:pone00:0038704
    DOI: 10.1371/journal.pone.0038704
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0038704
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0038704&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0038704?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
    ---><---

    References listed on IDEAS

    as
    1. Porter, Mason A. & Mucha, Peter J. & Newman, M.E.J. & Friend, A.J., 2007. "Community structure in the United States House of Representatives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 414-438.
    2. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    3. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    4. J. M. Kumpula & J. Saramäki & K. Kaski & J. Kertész, 2007. "Limited resolution in complex network community detection with Potts model approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 56(1), pages 41-45, March.
    5. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    6. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    7. Chen, P. & Redner, S., 2010. "Community structure of the physical review citation network," Journal of Informetrics, Elsevier, vol. 4(3), pages 278-290.
    8. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
    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. Zhu, Zhen & Morrison, Greg & Puliga, Michelangelo & Chessa, Alessandro & Riccaboni, Massimo, 2018. "The similarity of global value chains: A network-based measure," Network Science, Cambridge University Press, vol. 6(4), pages 607-632, December.

    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. Thang N. Dinh & Nam P. Nguyen & Md Abdul Alim & My T. Thai, 2015. "A near-optimal adaptive algorithm for maximizing modularity in dynamic scale-free networks," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 747-767, October.
    2. Wu, Ke & Liu, Xueming, 2021. "Community detection in directed acyclic graphs of adversary interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    3. Xiang, Ju & Tang, Yan-Ni & Gao, Yuan-Yuan & Zhang, Yan & Deng, Ke & Xu, Xiao-Ke & Hu, Ke, 2015. "Multi-resolution community detection based on generalized self-loop rescaling strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 127-139.
    4. Jing Yang & Yingwu Chen, 2011. "Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-5, July.
    5. Biao Xiong & Bixin Li & Rong Fan & Qingzhong Zhou & Wu Li, 2017. "Modeling and Simulation for Effectiveness Evaluation of Dynamic Discrete Military Supply Chain Networks," Complexity, Hindawi, vol. 2017, pages 1-9, October.
    6. 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.
    7. Erjia Yan & Ying Ding & Qinghua Zhu, 2010. "Mapping library and information science in China: a coauthorship network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 115-131, April.
    8. Nicholas S. Vonortas & Koichiro Okamura, 2013. "Network structure and robustness: lessons for research programme design," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 22(4), pages 392-411, June.
    9. Hayato Goto & Hideki Takayasu & Misako Takayasu, 2017. "Estimating risk propagation between interacting firms on inter-firm complex network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-12, October.
    10. Waleed Reafee & Naomie Salim & Atif Khan, 2016. "The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-20, May.
    11. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    12. Sameer Kumar & Bernd Markscheffel, 2016. "Bonded-communities in HantaVirus research: a research collaboration network (RCN) analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 533-550, October.
    13. Yu, Jiating & Wu, Ling-Yun, 2022. "Multiple Order Local Information model for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    14. Guillaume, Jean-Loup & Latapy, Matthieu, 2006. "Bipartite graphs as models of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 795-813.
    15. Foti, Nicholas J. & Pauls, Scott & Rockmore, Daniel N., 2013. "Stability of the World Trade Web over time – An extinction analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1889-1910.
    16. Chen, Mu & Yu, Boming & Xu, Peng & Chen, Jun, 2007. "A new deterministic complex network model with hierarchical structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 707-717.
    17. Davazdahemami, Behrooz & Kalgotra, Pankush & Zolbanin, Hamed M. & Delen, Dursun, 2023. "A developer-oriented recommender model for the app store: A predictive network analytics approach," Journal of Business Research, Elsevier, vol. 158(C).
    18. Haizheng Zhang & Baojun Qiu & Kristinka Ivanova & C. Lee Giles & Henry C. Foley & John Yen, 2010. "Locality and attachedness‐based temporal social network growth dynamics analysis: A case study of evolving nanotechnology scientific collaboration networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(5), pages 964-977, May.
    19. de Arruda, Guilherme F. & Costa, Luciano da Fontoura & Rodrigues, Francisco A., 2012. "A complex networks approach for data clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6174-6183.
    20. Perc, Matjaž, 2010. "Growth and structure of Slovenia’s scientific collaboration network," Journal of Informetrics, Elsevier, vol. 4(4), pages 475-482.

    More about this item

    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:plo:pone00:0038704. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.