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Communities as Well Separated Subgraphs with Cohesive Cores: Identification of Core-Periphery Structures in Link Communities

  • Frank Havemann
  • Jochen Gläser
  • Michael Heinz
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Communities in networks are commonly considered as highly cohesive subgraphs which are well separated from the rest of the network. However, cohesion and separation often cannot be maximized at the same time, which is why a compromise is sought by some methods. When a compromise is not suitable for the problem to be solved it might be advantageous to separate the two criteria. In this paper, we explore such an approach by defining communities as well separated subgraphs which can have one or more cohesive cores surrounded by peripheries. We apply this idea to link communities and present an algorithm for constructing core-periphery structures in link communities and first test results.

Keywords

Networks Communities Link clustering Core and periphery 

References

  1. 1.
    Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multi-scale complexity in networks. Nature 466, 761–764 (2010)Google Scholar
  2. 2.
    Amelio, A., Pizzuti, C.: Overlapping community discovery methods: a survey. In: Social Networks: Analysis and Case Studies, p. 105 (2014)Google Scholar
  3. 3.
    Bagrow, J.P., Bollt, E.M.: Local method for detecting communities. Phys. Rev. E 72(4), 046,108 (2005).  https://doi.org/10.1103/PhysRevE.72.046108Google Scholar
  4. 4.
    Ball, B., Karrer, B., Newman, M.E.J.: Efficient and principled method for detecting communities in networks. Phys. Rev. E 84(3), 036,103 (2011).  https://doi.org/10.1103/PhysRevE.84.036103Google Scholar
  5. 5.
    Borgatti, S.P., Everett, M.G.: Models of core/periphery structures. Soc. Netw. 21(4), 375–395 (2000)Google Scholar
  6. 6.
    Csermely, P., London, A., Wu, L.Y., Uzzi, B.: Structure and dynamics of core/periphery networks. J. Complex Netw. 1(2), 93–123 (2013).  https://doi.org/10.1093/comnet/cnt016Google Scholar
  7. 7.
    Evans, T.S., Lambiotte, R.: Line graphs, link partitions, and overlapping communities. Phys. Rev. E 80(1), 16,105 (2009)Google Scholar
  8. 8.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)Google Scholar
  9. 9.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99, 7821–7826 (2002)Google Scholar
  10. 10.
    Havemann, F., Gläser, J., Heinz, M.: Memetic search for overlapping topics based on a local evaluation of link communities. Scientometrics 1–30 (2017).  https://doi.org/10.1007/s11192-017-2302-5
  11. 11.
    Kannan, R., Vempala, S., Vetta, A.: On clusterings: good, bad and spectral. J. ACM 51(3), 497–515 (2004).  https://doi.org/10.1145/990308.990313Google Scholar
  12. 12.
    Kojaku, S., Masuda, N.: Finding multiple core-periphery pairs in networks. Phys. Rev. E 96(5), 052,313 (2017)Google Scholar
  13. 13.
    Leskovec, J., Lang, K.J., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 631–640, New York (2010).  https://doi.org/10.1145/1772690.1772755
  14. 14.
    Liu, D., Su, Y., Li, X., Niu, Z.: A novel community detection method based on cluster density peaks. In: Natural Language Processing and Chinese Computing. Lecture Notes in Computer Science, pp. 515–525. Springer (2017).  https://doi.org/10.1007/978-3-319-73618-1_43
  15. 15.
    Metzler, S., Günnemann, S., Miettinen, P.: Hyperbolae are no hyperbole: modelling communities that are not cliques. In: Data Mining (ICDM), 2016 IEEE 16th International Conference on Data Mining, pp. 330–339. IEEE (2016)Google Scholar
  16. 16.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026,113 (2004)Google Scholar
  17. 17.
    Piccardi, C.: Finding and testing network communities by lumped Markov chains. PloS one 6(11), e27,028 (2011)Google Scholar
  18. 18.
    Pizzuti, C.: A multi-objective genetic algorithm for community detection in networks. In: 21st IEEE International Conference on Tools with Artificial Intelligence, pp. 379–386. IEEE (2009)Google Scholar
  19. 19.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. PNAS 101, 2658–2663 (2004)Google Scholar
  20. 20.
    Ravasz, E., Barabási, A.L.: Hierarchical organization in complex networks. Phys. Rev. E 67(2), 026,112 (2003).  https://doi.org/10.1103/PhysRevE.67.026112Google Scholar
  21. 21.
    Rezvani, M., Wang, Q., Liang, W.: Fast Algorithm for Detecting Cohesive Hierarchies of Communities in Large Networks, pp. 486–494. ACM (2018).  https://doi.org/10.1145/3159652.3159704. http://dl.acm.org/citation.cfm?doid=3159652.3159704
  22. 22.
    Rossa, F.D., Dercole, F., Piccardi, C.: Profiling core-periphery network structure by random walkers. Sci. Rep. 3, 1467 (2013).  https://doi.org/10.1038/srep01467Google Scholar
  23. 23.
    Schaeffer, S.E.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007).  https://doi.org/10.1016/j.cosrev.2007.05.001Google Scholar
  24. 24.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000).  https://doi.org/10.1109/34.868688Google Scholar
  25. 25.
    Wang, X., Liu, G., Li, J., Nees, : J.P.: Locating structural centers: a density-based clustering method for community detection. PLOS ONE 12(1), e0169,355 (2017).  https://doi.org/10.1371/journal.pone.0169355
  26. 26.
    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).  https://doi.org/10.1145/2501654.2501657
  27. 27.
    Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD, KDD ’07, pp. 824–833. ACM, New York, NY, USA (2007).  https://doi.org/10.1145/1281192.1281280
  28. 28.
    Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2013).  https://doi.org/10.1007/s10115-013-0693-zGoogle Scholar
  29. 29.
    Yang, J., Leskovec, J.: Overlapping communities explain core-periphery organization of networks. Proc. IEEE 102(12), 1892–1902 (2014)Google Scholar
  30. 30.
    Zachary, W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)Google Scholar
  31. 31.
    Zhang, X., Martin, T., Newman, M.E.J.: Identification of core-periphery structure in networks. Phys. Rev. E 91(3), 032,803 (2015).  https://doi.org/10.1103/PhysRevE.91.032803. https://link.aps.org/doi/10.1103/PhysRevE.91.032803

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institut für Bibliotheks- und Informationswissenschaft, Humboldt-Universität zuBerlinGermany
  2. 2.Center for Technology and Society, TU BerlinBerlinGermany

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