IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0086891.html

Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks

Author

Listed:
  • Rodica Ioana Lung
  • Camelia Chira
  • Anca Andreica

Abstract

The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

Suggested Citation

  • Rodica Ioana Lung & Camelia Chira & Anca Andreica, 2014. "Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0086891
    DOI: 10.1371/journal.pone.0086891
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086891
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0086891&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0086891?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    2. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    3. McKelvey, Richard D. & McLennan, Andrew, 1996. "Computation of equilibria in finite games," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 2, pages 87-142, Elsevier.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Suciu Mihai-Alexandru & Gaskó Noémi & Lung Rodica Ioana, 2017. "Approximation of Nash equilibria and the network community structure detection problem," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-24, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ron N. Borkovsky & Ulrich Doraszelski & Yaroslav Kryukov, 2010. "A User's Guide to Solving Dynamic Stochastic Games Using the Homotopy Method," Operations Research, INFORMS, vol. 58(4-part-2), pages 1116-1132, August.
    2. Govindan, Srihari & Wilson, Robert, 2004. "Computing Nash equilibria by iterated polymatrix approximation," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1229-1241, April.
    3. Li Wang & Jiang Wang & Yuanjun Bi & Weili Wu & Wen Xu & Biao Lian, 2014. "Noise-tolerance community detection and evolution in dynamic social networks," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 600-612, October.
    4. P. Herings & Ronald Peeters, 2010. "Homotopy methods to compute equilibria in game theory," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(1), pages 119-156, January.
    5. Noah Stein & Asuman Ozdaglar & Pablo Parrilo, 2008. "Separable and low-rank continuous games," International Journal of Game Theory, Springer;Game Theory Society, vol. 37(4), pages 475-504, December.
    6. Tim Roughgarden, 2010. "Computing equilibria: a computational complexity perspective," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(1), pages 193-236, January.
    7. Peter Miltersen & Troels Sørensen, 2010. "Computing a quasi-perfect equilibrium of a two-player game," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(1), pages 175-192, January.
    8. Xiao Luo & Xuewen Qian & Yang Sun, 2021. "The algebraic geometry of perfect and sequential equilibrium: an extension," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 71(2), pages 579-601, March.
    9. Apiruk Detwarasiti & Ross D. Shachter, 2005. "Influence Diagrams for Team Decision Analysis," Decision Analysis, INFORMS, vol. 2(4), pages 207-228, December.
    10. Wu, Jianshe & Zhang, Long & Li, Yong & Jiao, Yang, 2016. "Partition signed social networks via clustering dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 568-582.
    11. Daniel Russo, 2023. "Approximation Benefits of Policy Gradient Methods with Aggregated States," Management Science, INFORMS, vol. 69(11), pages 6898-6911, November.
    12. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    13. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    14. P. Giovani Palafox-Alcantar & Dexter V. L. Hunt & Chris D. F. Rogers, 2020. "A Hybrid Methodology to Study Stakeholder Cooperation in Circular Economy Waste Management of Cities," Energies, MDPI, vol. 13(7), pages 1-30, April.
    15. Maurizio Iacopetta, 2014. "dynamics of assets liquidity and inequality in economies with decentralized markets," Working Papers hal-01099374, HAL.
    16. Amman, Hans M. & Kendrick, David A., 1998. "Computing the steady state of linear quadratic optimization models with rational expectations," Economics Letters, Elsevier, vol. 58(2), pages 185-191, February.
    17. John Stachurski, 2009. "Economic Dynamics: Theory and Computation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012774, December.
    18. Porter, Ryan & Nudelman, Eugene & Shoham, Yoav, 2008. "Simple search methods for finding a Nash equilibrium," Games and Economic Behavior, Elsevier, vol. 63(2), pages 642-662, July.
    19. Wu, Zhihao & Lin, Youfang & Wan, Huaiyu & Tian, Shengfeng & Hu, Keyun, 2012. "Efficient overlapping community detection in huge real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2475-2490.
    20. Hossein Sabzian & Mohammad Ali Shafia & Ali Maleki & Seyeed Mostapha Seyeed Hashemi & Ali Baghaei & Hossein Gharib, 2019. "Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners," Papers 1901.08932, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0086891. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.