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  • 标题:Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics
  • 本地全文:下载
  • 作者:Marialisa Scatá ; Barbara Attanasio ; Aurelio La Corte
  • 期刊名称:Future Internet
  • 电子版ISSN:1999-5903
  • 出版年度:2021
  • 卷号:13
  • 期号:5
  • 页码:135
  • DOI:10.3390/fi13050135
  • 出版社:MDPI Publishing
  • 摘要: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.
  • 关键词:6G networks; complex networks; multiplex social network; epidemic spreading; evolutionary game theory; cognitive networks; community detection 6G networks ; complex networks ; multiplex social network ; epidemic spreading ; evolutionary game theory ; cognitive networks ; community detection
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