Skip to main content
SpringerLink
Log in

Application of Coupled Dynamical Systems for Communities Detection in Complex Networks

  • Conference paper
Selected Topics in Nonlinear Dynamics and Theoretical Electrical Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 483))

  • 862 Accesses

Abstract

In this chapter we present a dynamical systems framework and its applications for stable communities detection and missing (or hidden) link predictions utilizing network topology and its dynamics. In particular, we consider the dynamical formulation of modularity extended with a random walk approach, and then generalize it to coupled dynamical systems to detect communities at different hierarchical levels. We introduce attractive and repulsive coupling and study different scenarios for dynamical links updates that allow us to make predictions on a cooperative or a competing behavior of users in the network and analyze connectivity dynamics. The developed methods are tested on benchmark networks and then applied for analysis of real-world mobile datasets to derive a social community structure and to make link predictions/recommendations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acebrón, J., Bonilla, L., Pérez-Vicente, C., et al.: The Kuramoto model: A simple paradigm for synchronization phenomena. Reviews of Modern Physics 77(1), 137–185 (2005)

    Article  Google Scholar 

  2. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Arenas, A., Díaz-Guilera, A., Pérez-Vicente, C.: Synchronization reveals topological scales in complex networks. Physical Review Letters 96, 114102 (2006)

    Article  Google Scholar 

  4. Arenas, A., Diaz-Guilera, A., Kurths, J., et al.: Synchronization in complex networks. Physics Reports 469, 93–153 (2008)

    Article  MathSciNet  Google Scholar 

  5. Blondel, V., Guillaume, J.L., Lambiotte, R., et al.: Fast unfolding of communites in large networks. Journal of Statistical Mechanics: Theory and Experiment 1742-5468(10), P10008+12 (2008)

    Google Scholar 

  6. Boccaletti, S., Latora, M.Y., et al.: Complex networks: Structure and dynamics. Physics Reports 424(4-5), 175–308 (2006)

    Article  MathSciNet  Google Scholar 

  7. Chung, F.R.K.: Spectral Graph Theory, CMBS Lectures Notes 92. Amer. Math. Society (1997)

    Google Scholar 

  8. Evans, T.S., Lambiotte, R.: Line Graphs, Link Partitions and Overlapping Communities. Physical Review E 80, 016105 (2009)

    Google Scholar 

  9. Flake, G., Lawrence, S., Giles, C.: Self-organization and identification of Web communities. IEEE Computer 35, 66–71 (2002)

    Article  Google Scholar 

  10. Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2011)

    Article  MathSciNet  Google Scholar 

  11. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  12. Ipsen, M., Mikhailov, A.: Evolutionary reconstruction of networks. Physical Review E 66, 046109 (2002)

    Google Scholar 

  13. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113 (2004)

    Google Scholar 

  14. Kiukkonen, N., Blom, J., Dousse, O., et al.: Towards Rich Mobile Phone Datasets: Lausanne Data Collection Campaign. In: Proc. ACM Int. Conf. Pervasive Services, Berlin (2010)

    Google Scholar 

  15. Kuramoto, Y.: Lectuer Notes in Physics, vol. 30. Springer NY (1975)

    Google Scholar 

  16. Lambiotte, R., Delvenne, J.C., Barahona, M.: Laplacian Dynamics and Multiscale Modular Structure in Networks. ArXiv:0812.1770v3 (2009)

    Google Scholar 

  17. Liben-Nowel, D., Kleinberg, J.: The Link Prediction Problem for Social Networks. ACM Int. Conf. on Information and Knowledge Management (2003)

    Google Scholar 

  18. Nefedov, N.: Multiple-Membership Communities Detection in Mobile Networks. In: Proc. ACM Int. Conf. on Web Intelligence, Mining and Semantics (WIMS 2011), Norway (2011)

    Google Scholar 

  19. Nefedov, N.: Applications of System Dynamics for Communities Detection in Complex Networks. In: IEEE Int. Conf. on Nonlinear Dynamics and Sync (INDS 2011), Austria (2011)

    Google Scholar 

  20. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review, E 69, 066133 (2004)

    Google Scholar 

  21. Olfati-Saber, R., et al.: Consensus and Cooperation in Networked Multi-Agent Systems. IEEE Proceedings 95(1), 215–233 (2007)

    Article  Google Scholar 

  22. Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)

    Google Scholar 

  23. Zachary, W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nokia Research Center, Lausanne, Switzerland

    Nikolai Nefedov

  2. ISI Lab, Swiss Federal Institute of Technology, Zurich (ETHZ), Switzerland

    Nikolai Nefedov

Authors
  1. Nikolai Nefedov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikolai Nefedov .

Editor information

Editors and Affiliations

  1. Institut für Intelligente, Systemtechnol0, Alpen Adria Universität Klagenfurt, Universitätsstraße 65-67, Klagenfurt, 9020, Austria

    Kyandoghere Kyamakya

  2. , Lehrgebiet Informationstechnik, FernUniversität in Hagen, Universitätsstraße 27, Hagen, 58097, Germany

    Wolfgang A. Halang

  3. , Institut für Theoretische, Leibniz Universität Hannover, Appelstr. 9A, Hannover, 30167, Germany

    Wolfgang Mathis

  4. , Lakeside Park, Alpen-Adria Universität Klagenfurt, Haus B04, Klagenfurt, 9020, Austria

    Jean Chamberlain Chedjou

  5. Fernuniversität Hagen, Philipp-Reis-Gebäude, Hagen, 58084, Germany

    Zhong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nefedov, N. (2013). Application of Coupled Dynamical Systems for Communities Detection in Complex Networks. In: Kyamakya, K., Halang, W., Mathis, W., Chedjou, J., Li, Z. (eds) Selected Topics in Nonlinear Dynamics and Theoretical Electrical Engineering. Studies in Computational Intelligence, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37781-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37781-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37780-8

  • Online ISBN: 978-3-642-37781-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation