IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v554y2020ics0378437120300704.html
   My bibliography  Save this article

Preferential interaction networks: A dynamic model for brain synchronization networks

Author

Listed:
  • Sousa, R.A.
  • Lula-Rocha, V.N.A.
  • Toutain, T.
  • Rosário, R.S.
  • Cambui, E.C.B.
  • Miranda, J.G.V.

Abstract

In this work, we propose a weighted network model called a preferential interaction network (PIN), whose construction is based on mechanisms similar to those of the scale-free network model developed by Albert and Barabási. The PIN aims to reproduce the behaviour of real systems of fixed size in which stronger connections are reinforced over time. Its composition starts with a network with fixed nodes, and obeys two basic processes: firstly, an increase in the edge weights, and secondly, a preferential interaction is added. Preferential interaction is defined as the tendency whereby edges with larger weights are more likely to be chosen in a iteration. PIN has three parameters: the size of the network, the rate of increase of the weights, and the number of iterations. These parameters were used to fit the model to EEG brain synchronization networks of healthy subjects. The complementary cumulative distribution of weight in the model was compared to the corresponded distribution in EEG networks. The shape of the model distribution showed similar to those of the EEG data networks and the PIN model topology index are closer than a random null model, suggesting that a preferential interaction process represents the core of brain synchronization dynamics.

Suggested Citation

  • Sousa, R.A. & Lula-Rocha, V.N.A. & Toutain, T. & Rosário, R.S. & Cambui, E.C.B. & Miranda, J.G.V., 2020. "Preferential interaction networks: A dynamic model for brain synchronization networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    • Handle: RePEc:eee:phsmap:v:554:y:2020:i:c:s0378437120300704
    DOI: 10.1016/j.physa.2020.124259
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120300704
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.124259?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rosário, R.S. & Cardoso, P.T. & Muñoz, M.A. & Montoya, P. & Miranda, J.G.V., 2015. "Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 7-19.
    2. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780198515906.
    3. Barthélemy, Marc & Barrat, Alain & Pastor-Satorras, Romualdo & Vespignani, Alessandro, 2005. "Characterization and modeling of weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 34-43.
    4. Li, Menghui & Wu, Jinshan & Wang, Dahui & Zhou, Tao & Di, Zengru & Fan, Ying, 2007. "Evolving model of weighted networks inspired by scientific collaboration networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(1), pages 355-364.
    5. Li, Chunguang & Chen, Guanrong, 2006. "Modelling of weighted evolving networks with community structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 869-876.
    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. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).

    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. repec:hal:spmain:info:hdl:2441/9795 is not listed on IDEAS
    2. Giorgio Fagiolo & Javier Reyes & Stefano Schiavo, 2010. "The evolution of the world trade web: a weighted-network analysis," Journal of Evolutionary Economics, Springer, vol. 20(4), pages 479-514, August.
    3. Fagiolo, Giorgio & Reyes, Javier & Schiavo, Stefano, 2008. "On the topological properties of the world trade web: A weighted network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3868-3873.
    4. Giorgio Fagiolo & Javier Reyes & Stefano Schiavo, 2007. "The Evolution of the World Trade Web," LEM Papers Series 2007/17, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Hellmann, Tim & Staudigl, Mathias, 2014. "Evolution of social networks," European Journal of Operational Research, Elsevier, vol. 234(3), pages 583-596.
    6. Giorgio Fagiolo, 2010. "The international-trade network: gravity equations and topological properties," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(1), pages 1-25, June.
    7. Stanislaw Drozdz & Andrzej Kulig & Jaroslaw Kwapien & Artur Niewiarowski & Marek Stanuszek, 2017. "Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation," Papers 1705.06208, arXiv.org, revised Oct 2017.
    8. Ya-Chun Gao & Zong-Wen Wei & Bing-Hong Wang, 2013. "Dynamic Evolution Of Financial Network And Its Relation To Economic Crises," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-10.
    9. Vinayak, & Raghuvanshi, Adarsh & kshitij, Avinash, 2023. "Signatures of capacity development through research collaborations in artificial intelligence and machine learning," Journal of Informetrics, Elsevier, vol. 17(1).
    10. Zhou, Wei-Xing & Jiang, Zhi-Qiang & Sornette, Didier, 2007. "Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 741-752.
    11. Bezsudnov, I.V. & Snarskii, A.A., 2014. "From the time series to the complex networks: The parametric natural visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 53-60.
    12. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    13. Wang, Qingyun & Duan, Zhisheng & Chen, Guanrong & Feng, Zhaosheng, 2008. "Synchronization in a class of weighted complex networks with coupling delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5616-5622.
    14. Lemarchand, Guillermo A., 2012. "The long-term dynamics of co-authorship scientific networks: Iberoamerican countries (1973–2010)," Research Policy, Elsevier, vol. 41(2), pages 291-305.
    15. F. W. S. Lima, 2015. "Evolution of egoism on semi-directed and undirected Barabási-Albert networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(12), pages 1-9.
    16. G. Ghoshal & M. E.J. Newman, 2007. "Growing distributed networks with arbitrary degree distributions," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 58(2), pages 175-184, July.
    17. Chang, Y.F. & Han, S.K. & Wang, X.D., 2018. "The way to uncover community structure with core and diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 111-119.
    18. Chakrabarti, Anindya S., 2015. "Stochastic Lotka-Volterra equations: A model of lagged diffusion of technology in an interconnected world," IIMA Working Papers WP2015-08-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Linda Margarita Medina Herrera & José Benito Díaz Hernández, 2011. "Caracterización y modelado de redes: el caso de la Bolsa Mexicana de Valores," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 5(1), pages 23-32.
    20. Roth, Camille, 2007. "Empiricism for descriptive social network models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 53-58.
    21. Douglas R. White & Jason Owen-Smith & James Moody & Walter W. Powell, 2004. "Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings," Computational and Mathematical Organization Theory, Springer, vol. 10(1), pages 95-117, May.

    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:eee:phsmap:v:554:y:2020:i:c:s0378437120300704. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.