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Discovering Communities through Friendship

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    3. Chen, P. & Redner, S., 2010. "Community structure of the physical review citation network," Journal of Informetrics, Elsevier, vol. 4(3), pages 278-290.
    4. 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.
    5. 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.
    6. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    7. 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.
    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.
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    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.

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