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Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics

Author

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  • Marialisa Scatá

    (Dipartimento di Ingegneria Elettrica, Elettronica ed Informatica (DIEEI), Universitá di Catania, 95125 Catania, Italy)

  • Barbara Attanasio

    (Dipartimento di Ingegneria Elettrica, Elettronica ed Informatica (DIEEI), Universitá di Catania, 95125 Catania, Italy)

  • Aurelio La Corte

    (Dipartimento di Ingegneria Elettrica, Elettronica ed Informatica (DIEEI), Universitá di Catania, 95125 Catania, Italy)

Abstract

Complex systems are fully described by the connectedness of their elements studying how these develop a collective behavior, interacting with each other following their inner features, and the structure and dynamics of the entire system. The forthcoming 6G will attempt to rewrite the communication networks’ perspective, focusing on a radical revolution in the way entities and technologies are conceived, integrated and used. This will lead to innovative approaches with the aim of providing new directions to deal with future network challenges posed by the upcoming 6G, thus the complex systems could become an enabling set of tools and methods to design a self-organized, resilient and cognitive network, suitable for many application fields, such as digital health or smart city living scenarios. Here, we propose a complex profiling approach of heterogeneous nodes belonging to the network with the goal of including the multiplex social network as a mathematical representation that enables us to consider multiple types of interactions, the collective dynamics of diffusion and competition, through social contagion and evolutionary game theory, and the mesoscale organization in communities to drive learning and cognition. Through a framework, we detail the step by step modeling approach and show and discuss our findings, applying it to a real dataset, by demonstrating how the proposed model allows us to detect deeply complex knowable roles of nodes.

Suggested Citation

  • Marialisa Scatá & Barbara Attanasio & Aurelio La Corte, 2021. "Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics," Future Internet, MDPI, vol. 13(5), pages 1-17, May.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:5:p:135-:d:558230
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    References listed on IDEAS

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    1. Suvodeep Mazumdar & Dhavalkumar Thakker, 2020. "Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks," Future Internet, MDPI, vol. 12(12), pages 1-22, November.
    2. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    3. Raul J Mondragon & Jacopo Iacovacci & Ginestra Bianconi, 2018. "Multilink communities of multiplex networks," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-15, March.
    4. A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 13(3), pages 547-560, February.
    5. Dongwei Guo & Mengmeng Fu & Hai Li, 2021. "Cooperation in Social Dilemmas: A Group Game Model with Double-Layer Networks," Future Internet, MDPI, vol. 13(2), pages 1-27, January.
    6. Swami Iyer & Timothy Killingback, 2016. "Evolution of Cooperation in Social Dilemmas on Complex Networks," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-25, February.
    7. Manlio De Domenico & Vincenzo Nicosia & Alexandre Arenas & Vito Latora, 2015. "Structural reducibility of multilayer networks," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
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    1. Marialisa Scatá & Aurelio La Corte, 2023. "A Complex Insight for Quality of Service Based on Spreading Dynamics and Multilayer Networks in a 6G Scenario," Mathematics, MDPI, vol. 11(2), pages 1-20, January.

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