Dynamics in Complex Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 1611

Special Issue Editors


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Guest Editor
School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Interests: complex networks; evolutionary games

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Guest Editor
School of Business, East China University of Science and Technology, Shanghai 200237, China
Interests: econophysics; sociophysics; complex economic networks; complex financial networks; fractal analysis; cooperation
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Guest Editor
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: evolutionary game theory; multi-agent coordination games and control; group decision-making and group intelligence
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Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China
Interests: statistical physics; complex systems; spin system dynamics

Special Issue Information

Dear Colleagues,

Complex networks have attracted a lot of attention among researchers. Many of them exhibit an array of structures at different scales. In this Special Issue, dynamics taking place in complex networks, such as cooperation and percolation, will be widely discussed. In particular, one emphasis is on how observations of such dynamics may be used to infer multilayer networks and higher-order network structure.

Multilayer Networks and Higher-Order Networks: The network is a convenient mathematical model to represent the structure of a complex system. Multilayer network science has proved to be an effective modeling and analysis framework applicable to a wide research area manifesting from biopolymer networks to social networks. Higher-order networks describe the multibody interactions of various complex systems ranging from the brain to the collaborative network, which have become the preferred representation for capturing the underlying network topology and geometry of complex systems. 

Evolution of Collective Behaviors and Percolation on Complex Networks: Individuals can interact in evolutionary games and percolation on social networks. Such interactions may provide insights into the evolution of network structures and dynamics on networks. The development and challenges of those dynamics researches in multilayer and higher-order networks have attracted extensive attention. 

Experimental Advances on Social Dilemma Games: Various situations in human social life can be considered social dilemmas, such as those in healthcare and business management. Researchers in this field tend to focus on two aspects in social dilemma games: the root of cooperation mechanisms in laboratory scenarios and the design of cooperation mechanisms in field experiments. Other recent experimental advances on social dilemma games are also welcome in this Special Issue.

Dr. Luoluo Jiang
Prof. Dr. Zhi-Qiang Jiang
Prof. Dr. Xiaojie Chen
Dr. Zhi Chen
Guest Editors

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Keywords

  • complex networks
  • dynamics
  • multilayer networks and higher-order networks
  • evolution of collective behaviors and percolation on complex networks
  • mathematical model
  • evolutionary games
  • network structures
  • dynamics on networks

Published Papers (1 paper)

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Research

16 pages, 951 KiB  
Article
Dynamic Complex Network, Exploring Differential Evolution Algorithms from Another Perspective
by Yifei Yang, Sichen Tao, Haichuan Yang, Zijing Yuan and Zheng Tang
Mathematics 2023, 11(13), 2979; https://doi.org/10.3390/math11132979 - 4 Jul 2023
Cited by 2 | Viewed by 1040
Abstract
Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed by these connections, known as complex networks, embody the underlying principles governing the system’s behavior. While complex networks have been previously applied in the [...] Read more.
Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed by these connections, known as complex networks, embody the underlying principles governing the system’s behavior. While complex networks have been previously applied in the field of evolutionary computation, prior studies have been limited in their ability to reach conclusive conclusions. Based on our investigations, we are against the notion that there is a direct link between the complex network structure of an algorithm and its performance, and we demonstrate this experimentally. In this paper, we address these limitations by analyzing the dynamic complex network structures of five algorithms across three different problems. By incorporating mathematical distributions utilized in prior research, we not only generate novel insights but also refine and challenge previous conclusions. Specifically, we introduce the biased Poisson distribution to describe the algorithm’s exploration capability and the biased power-law distribution to represent its exploitation potential during the convergence process. Our aim is to redirect research on the interplay between complex networks and evolutionary computation towards dynamic network structures, elucidating the essence of exploitation and exploration in the black-box optimization process of evolutionary algorithms via dynamic complex networks. Full article
(This article belongs to the Special Issue Dynamics in Complex Networks)
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