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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2018: Recent Trends and Future Technology in Applied Intelligence pp 670–676Cite as

Detecting Overlapping Communities in Social Networks with Voronoi and Tolerance Rough Sets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10868))

Abstract

In this work, we propose a novel method based on Voronoi diagrams and tolerance rough set method (TRSM) to detect overlapping communities. In the proposed Voronoi TRSM approach, a social network is represented as a graph. A Voronoi diagram is a partitioning of a plane into regions based on closeness to points in a specific set of sites (seeds). These seeds are used as a core for determining tolerance classes. The upper approximation operator from TRSM is used to obtain overlapping nodes. We have experimented with three well-known real networks and compared with Fuzzy-Rough and a Matrix Factorization-based approach. The results with proposed Voronoi TRSM approach are promising in terms of the extended modularity measure and the dense communities measure.

This research has been supported by the NSERC Discovery grants program and the Queen Elizabeth II Scholarship Program.

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References

  1. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  2. Campigotto, R., Céspedes, P.C., Guillaume, J.L.: A generalized and adaptive method for community detection. ArXiv preprint arXiv:1406.2518 (2014)

  3. Cao, X., Wang, X., Jin, D., Cao, Y., He, D.: Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization. Sci. Rep. 3, 2993 (2013)

    Article  Google Scholar 

  4. Chen, Q., Wu, T.T., Fang, M.: Detecting local community structures in complex networks based on local degree central nodes. Phys. A Stat. Mech. Appl. 392(3), 529–537 (2013)

    Article  Google Scholar 

  5. Deritei, D., Lázár, Z.I., Papp, I., Járai-Szabó, F., Sumi, R., Varga, L., Regan, E.R., Ercsey-Ravasz, M.: Community detection by graph Voronoi diagrams. New J. Phys. 16(6), 063007 (2014)

    Article  Google Scholar 

  6. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  7. Kundu, S., Pal, S.K.: Fuzzy-rough community in social networks. Pattern Recogn. Lett. 67, 145–152 (2015)

    Article  Google Scholar 

  8. Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033015 (2009)

    Article  Google Scholar 

  9. Lusseau, D.: The emergent properties of a dolphin social network. Proc. Roy. Soc. Lond. B Biol. Sci. 270(Suppl 2), S186–S188 (2003)

    Article  Google Scholar 

  10. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks (2004). http://arxiv.org/pdf/cond-mat/0308217v1:PDF

  11. Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)

    Article  Google Scholar 

  12. Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)

    Article  Google Scholar 

  13. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)

    Article  Google Scholar 

  14. Peters, J., Ramanna, S.: Proximal three-way decisions: theory and applications in social networks. Knowl. Based Syst. Elsevier 91, 4–15 (2016)

    Article  Google Scholar 

  15. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Nat. Acad. Sci. USA 101(9), 2658–2663 (2004)

    Article  Google Scholar 

  16. Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Nat. Acad. Sci. 105(4), 1118–1123 (2008)

    Article  Google Scholar 

  17. Shen, H., Cheng, X., Cai, K., Hu, M.B.: Detect overlapping and hierarchical community structure in networks. Phys. A Stat. Mech. Appl. 388(8), 1706–1712 (2009)

    Article  Google Scholar 

  18. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27(2, 3), 245–253 (1996)

    MathSciNet  MATH  Google Scholar 

  19. Whang, J.J., Gleich, D.F., Dhillon, I.S.: Overlapping community detection using seed set expansion. In: Proceedings of ACM International Conference onIinformation & Knowledge Management, pp. 2099–2108 (2013)

    Google Scholar 

  20. Wu, H., Gao, L., Dong, J., Yang, X.: Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks. PLoS ONE 9(3), e91856 (2014)

    Article  Google Scholar 

  21. Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 43:1–43:35 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, R3B 2E9, Canada

    Kushagra Trivedi & Sheela Ramanna

Authors
  1. Kushagra Trivedi
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  2. Sheela Ramanna
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Corresponding author

Correspondence to Sheela Ramanna .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Malek Mouhoub

  2. University of Regina, Regina, SK, Canada

    Samira Sadaoui

  3. Concordia University, Montreal, QC, Canada

    Otmane Ait Mohamed

  4. Texas State University, San Marcos, TX, USA

    Moonis Ali

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Trivedi, K., Ramanna, S. (2018). Detecting Overlapping Communities in Social Networks with Voronoi and Tolerance Rough Sets. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_64

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  • DOI: https://doi.org/10.1007/978-3-319-92058-0_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92057-3

  • Online ISBN: 978-3-319-92058-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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