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Topological transition in a coupled dynamics in random networks

Author

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  • Gomes, P.F.
  • Fernandes, H.A.
  • Costa, A.A.

Abstract

In this work, we study the topological transition on the associated networks in a model proposed by Saeedian et al. (Scientific Reports 2019 9:9726), which considers a coupled dynamics of node and link states. Our goal was to better understand the two observed phases, so we use another network structure (the so called random geometric graph — RGG) together with other metrics borrowed from network science. We observed a topological transition on the two associated networks, which are subgraphs of the full network. As the links have two possible states (friendly and non-friendly), we defined each associated network as composed of only one type of link. The (non) friendly associated network has (non) friendly links only. This topological transition was observed from the domain distribution of each associated network between the two phases of the system (absorbing and active). We also showed that another metric from network science called modularity (or assortative coefficient) can also be used as order parameter, giving the same phase diagram as the original order parameter from the seminal work. On the absorbing phase the absolute value of the modularity for each associated network reaches a maximum value, while on the active phase they fall to the minimum value.

Suggested Citation

  • Gomes, P.F. & Fernandes, H.A. & Costa, A.A., 2022. "Topological transition in a coupled dynamics in random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
  • Handle: RePEc:eee:phsmap:v:597:y:2022:i:c:s0378437122002333
    DOI: 10.1016/j.physa.2022.127269
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    References listed on IDEAS

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    1. Sandro M. Reia & Paulo F. Gomes & José F. Fontanari, 2019. "Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(9), pages 1-10, September.
    2. Sandro M. Reia, 2020. "Diffusion of innovations in Axelrod’s model on small-world networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(08), pages 1-11, August.
    3. Avin, Chen & Daltrophe, Hadassa & Keller, Barbara & Lotker, Zvi & Mathieu, Claire & Peleg, David & Pignolet, Yvonne-Anne, 2020. "Mixed preferential attachment model: Homophily and minorities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    4. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    5. Klemm, Konstantin & Eguı́luz, Vı́ctor M & Toral, Raúl & Miguel, Maxi San, 2003. "Role of dimensionality in Axelrod's model for the dissemination of culture," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 327(1), pages 1-5.
    6. Hernández, Alexis R. & Gracia-Lázaro, Carlos & Brigatti, Edgardo & Moreno, Yamir, 2018. "Robustness of cultural communities in an open-ended Axelrod’s model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 492-500.
    7. Han, Wenchen & Feng, Yuee & Qian, Xiaolan & Yang, Qihui & Huang, Changwei, 2020. "Clusters and the entropy in opinion dynamics on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
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