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Complex Networks pp 87–98Cite as

Stable Community Cores in Complex Networks

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Part of the Studies in Computational Intelligence book series (SCI,volume 424)

Abstract

Complex networks are generally composed of dense sub-networks called communities. Many algorithms have been proposed to automatically detect such communities. However, they are often unstable and behave nondeterministically. We propose here to use this non-determinism in order to compute groups of nodes on which community detection algorithms agree most of the time.We show that these groups of nodes, called community cores, are more similar to Ground Truth than communities in real and artificial networks. Furthermore, we show that in contrary to the classical approaches, we can reveal the absence of community structure in random graphs.

Keywords

  • Complex Network
  • Random Graph
  • Random Network
  • Community Detection
  • Community Core

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Aynaud, T., Guillaume, J.: Static community detection algorithms for evolving networks. In: Proceedings of the 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), pp. 513–519. IEEE (2010)

    Google Scholar 

  2. Blondel, V., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, P10,008 (2008)

    Google Scholar 

  3. Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On Finding Graph Clusterings with Maximum Modularity. In: Brandstädt, A., Kratsch, D., Müller, H. (eds.) WG 2007. LNCS, vol. 4769, pp. 121–132. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  4. Erdös, P., Rényi, A.: On random graphs. i. Publicationes Mathematicae (Debrecen) 6, 290–297 (1959)

    MATH  Google Scholar 

  5. Gfeller, D., Chappelier, J., De Los Rios, P.: Finding instabilities in the community structure of complex networks. Physical Review E 72(5), 056, 135 (2005)

    CrossRef  Google Scholar 

  6. Girvan, M., Newman, M.: Community structure in social and biological networks. In: Proceedings of the National Academy of Sciences, vol. 99(12), p. 7821 (2002)

    Google Scholar 

  7. Guimera, R., Danon, L., Diaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Physical Review E 68(6), 065, 103 (2003)

    CrossRef  Google Scholar 

  8. Guimera, R., Sales-Pardo, M., Amaral, L.: Modularity from fluctuations in random graphs and complex networks. Physical Review E 70(2), 025, 101 (2004)

    CrossRef  Google Scholar 

  9. Karrer, B., Levina, E., Newman, M.: Robustness of community structure in networks. Physical Review E 77(4), 046, 119 (2008)

    CrossRef  Google Scholar 

  10. Lambiotte, R.: Multi-scale modularity in complex networks. In: 2010 Proceedings of the 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt). IEEE (2010)

    Google Scholar 

  11. Lancichinetti, A.: Community detection algorithms: a comparative analysis. Physical Review E 80(5), 056, 117 (2009)

    CrossRef  Google Scholar 

  12. Molloy, M., Reed, B.: A critical point for random graphs with a given degree sequence. Random Structures & Algorithms 6(2-3), 161–180 (1995)

    MathSciNet  MATH  CrossRef  Google Scholar 

  13. Newman, M.: The structure of scientific collaboration networks. In: Proceedings of the National Academy of Sciences, vol. 98(2), p. 404 (2001)

    Google Scholar 

  14. Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical review E 69(2), 026, 113 (2004)

    CrossRef  Google Scholar 

  15. Pfitzner, D., Leibbrandt, R., Powers, D.: Characterization and evaluation of similarity measures for pairs of clusterings. Knowledge and Information Systems 19(3), 361–394 (2009)

    CrossRef  Google Scholar 

  16. Qinna, W., Fleury, E.: Detecting overlapping communities in graphs. In: European Conference on Complex Systems (ECCS 2009), Warwick Royaume-Uni. (2009), http://hal.inria.fr/inria-00398817/en/

  17. Rosvall, M., Bergstrom, C.: Mapping change in large networks. PloS one 5(1), e8694 (2010)

    CrossRef  Google Scholar 

  18. Salwinski, L., Miller, C., Smith, A., Pettit, F., Bowie, J., Eisenberg, D.: The database of interacting proteins: 2004 update. Nucleic Acids Research 32(suppl. 1), D449–D451 (2004)

    CrossRef  Google Scholar 

  19. Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research 3, 583–617 (2003)

    MathSciNet  MATH  Google Scholar 

  20. Vinh, N., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: is a correction for chance necessary? In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1073–1080. ACM (2009)

    Google Scholar 

  21. Zachary, W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 452–473 (1977)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory of Computer Sciences, Paris VI (LIP6), Pierre and Marie Curie University, 4, place Jussieu, 75005, Paris, France

    Massoud Seifi & Jean-Loup Guillaume

  2. Centre for Genomic Regulation (CRG), Barcelona, Spain

    Ivan Junier, Jean-Baptiste Rouquier & Svilen Iskrov

  3. Institute of Complex Systems, Paris le-de-France, 57/59, rue Lhomond, 75005, Paris, France

    Ivan Junier, Jean-Baptiste Rouquier & Svilen Iskrov

Authors
  1. Massoud Seifi
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  2. Ivan Junier
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  3. Jean-Baptiste Rouquier
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  4. Svilen Iskrov
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  5. Jean-Loup Guillaume
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Corresponding author

Correspondence to Massoud Seifi .

Editor information

Editors and Affiliations

  1. Dept. Computer Sciences, Florida Institute of Technology, W. University Blvd. 150, Melbourne, FL, 32901, Florida, USA

    Ronaldo Menezes

  2. COPPE/Federal Univ. of Rio de Janeiro, Rio de Janeiro, RJ, 21941-972, Brazil

    Alexandre Evsukoff

  3. Massachusetts Institute of Technology, Room 1-153, Massachusetts Avenue 77, Cambridge, 02139, USA

    Marta C. González

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Seifi, M., Junier, I., Rouquier, JB., Iskrov, S., Guillaume, JL. (2013). Stable Community Cores in Complex Networks. In: Menezes, R., Evsukoff, A., González, M. (eds) Complex Networks. Studies in Computational Intelligence, vol 424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30287-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-30287-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

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