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Mathematics
Special Issues
Dynamic Analysis and Decision-Making in Complex Networks
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Dynamic Analysis and Decision-Making in Complex Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 January 2026 | Viewed by 2356

Special Issue Editor

Dr. Dun Han

E-Mail Website
Guest Editor
School of Mathematical Sciences, Jiangsu University, Zhenjiang 212013, China
Interests: complex networks and communication dynamics; intelligent decision-making and nonlinear complex systems

Special Issue Information

Dear Colleagues,

This Special Issue focuses on dynamic behaviors and decision-making mechanisms within complex networks, which are fundamental to understanding and managing real-world systems such as social networks, transportation systems, power grids, and biological networks. We invite high-quality contributions that explore theoretical models, computational methods, and applications involving dynamic processes—such as diffusion, synchronization, control, or game–theoretic interactions—in evolving or multilayer network structures. Topics of interest include, but are not limited to, the following: networked decision dynamics, emergent behaviors, stability analysis, optimal control, and data-driven modeling approaches. By bringing together cutting-edge research from mathematics, systems science, and applied domains, this Special Issue aims to promote interdisciplinary advancements in the analysis, prediction, and design of intelligent and resilient networked systems.

We look forward to your submissions.

Dr. Dun Han
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • complex networks
  • dynamic systems
  • network decision-making
  • evolutionary game theory
  • diffusion and propagation
  • multi-agent systems
  • stability and control
  • network optimization
  • multilayer networks
  • data-driven modeling

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Published Papers (4 papers)

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Research

22 pages, 2219 KB  
Article
How Does Government Innovation Regulation Inhibit Corporate “Greenwashing”?—Based on a Tripartite Evolutionary Game Perspective
by Yuqing Zhu, Mengyun Wu, Jie Lu and Qi Jiang
Mathematics 2025, 13(22), 3658; https://doi.org/10.3390/math13223658 - 14 Nov 2025
Viewed by 353
Abstract
A strategic fulcrum for leading high-quality economic development and shaping the nation’s future. Core competitiveness lies in how governments can effectively stimulate consumer demand for green consumption and motivate enterprises to pursue green technology innovation through the development of precise and efficient innovative [...] Read more.
A strategic fulcrum for leading high-quality economic development and shaping the nation’s future. Core competitiveness lies in how governments can effectively stimulate consumer demand for green consumption and motivate enterprises to pursue green technology innovation through the development of precise and efficient innovative regulation models. In this paper, a tripartite evolutionary game model is constructed based on evolutionary game theory, encompassing the government, enterprises, and consumers. We analyze the strategic interactions and evolutionary path among these three entities under conditions of bounded rationality and information asymmetry. The research reveals the following: (1) the government can effectively guide enterprises towards genuine green innovation through enhanced rewards for substantive innovation and increased penalties for strategic innovation; (2) consumer purchasing decisions are significantly shaped by economic benefits, perceived social value, and government subsidies, with their market choices forming a critical external supervisory force; and (3) government regulatory strategies are dynamically adjusted in response to market integrity levels and social welfare, with a tendency to implement innovative regulation when “greenwashing” risk is elevated. In conclusion, simulation analysis is conducted using MATLAB 2018a, and governance recommendations are offered based on three dimensions: precise government regulation, enhanced corporate responsibility, and enhanced consumer capabilities. These recommendations offer both a theoretical basis and a practical path for establishing an integrated green innovation governance system based on incentive constraint empowerment. Full article
(This article belongs to the Special Issue Dynamic Analysis and Decision-Making in Complex Networks)
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21 pages, 4923 KB  
Article
Dynamic Analysis of China’s Urban Economic Spatial Network and Its Multidimensional Impact on Building Carbon Emissions
by Juan Li and Mei Sun
Mathematics 2025, 13(21), 3415; https://doi.org/10.3390/math13213415 - 27 Oct 2025
Cited by 1 | Viewed by 406
Abstract
With the continuous development of cities, the network connections between Chinese cities have rapidly strengthened, and cities are gradually transforming from traditional production bases into economic platforms within dynamic spaces. In this process, urban building carbon emissions are not only determined by the [...] Read more.
With the continuous development of cities, the network connections between Chinese cities have rapidly strengthened, and cities are gradually transforming from traditional production bases into economic platforms within dynamic spaces. In this process, urban building carbon emissions are not only determined by the city’s own resource and industrial advantages but are increasingly influenced by its position within the urban economic space network. This study constructs an urban economic spatial network using the gravity model, and based on dynamic data of building carbon emissions in Chinese cities from 2008 to 2020, develops a new analytical framework from the perspective of dynamic network evolution to examine the impact mechanisms of urban network position and residential activity intensity on building carbon emissions. The findings indicate that both residents’ activity intensity and city’s network position have a significant positive impact on per capita building carbon emissions, The impact coefficient between residential activity intensity and per capita building carbon emissions is 0.278 (p < 0.01). This conclusion remains valid after robustness and endogeneity tests. The city’s network position can mitigate the detrimental impact that residents’ activity intensity has on per capita building carbon emissions, particularly in the dynamic decision-making process, where cities can adjust their strategies based on their network position. The influence of city’s network position on per capita building carbon emissions exhibits multidimensional heterogeneity, with its effect being more significant in megalopolis and metropolis compared to large city and medium & small city. Specifically, in megalopolis, the network position impact coefficient is 0.22, significantly higher than 0.039 in medium & small city. These findings provide new perspectives for reducing building carbon emissions at the urban-level in the context of dynamic spatial mobility. Full article
(This article belongs to the Special Issue Dynamic Analysis and Decision-Making in Complex Networks)
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11 pages, 285 KB  
Article
Toward a Distributed Potential Game Optimization to Sensor Area Coverage Problem
by Jun Huang, Jie Chen, Rongcheng Dong, Xinli Xiong and Simao Xu
Mathematics 2025, 13(17), 2709; https://doi.org/10.3390/math13172709 - 22 Aug 2025
Viewed by 532
Abstract
The sensor coverage problem is a well-known combinatorial optimization problem that continues to attract the attention of many researchers. The existing game-based algorithms mainly pursue a feasible solution when solving this problem. This problem is described as a potential game, and a memory-based [...] Read more.
The sensor coverage problem is a well-known combinatorial optimization problem that continues to attract the attention of many researchers. The existing game-based algorithms mainly pursue a feasible solution when solving this problem. This problem is described as a potential game, and a memory-based greedy learning (MGL) algorithm is proposed, which can ensure convergence to Nash equilibrium. Compared with existing representative algorithms, our proposed algorithm performs the best in terms of average coverage, best value, and standard deviation within within a suitable time. In addition, increasing memory length helps to generate a better Nash equilibrium. Full article
(This article belongs to the Special Issue Dynamic Analysis and Decision-Making in Complex Networks)
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15 pages, 656 KB  
Article
Green Technology Game and Data-Driven Parameter Identification in the Digital Economy
by Xiaofeng Li and Qun Zhao
Mathematics 2025, 13(14), 2302; https://doi.org/10.3390/math13142302 - 18 Jul 2025
Viewed by 495
Abstract
The digital economy presents multiple challenges to the promotion of green technologies, including behavioral uncertainty among firms, heterogeneous technological choices, and disparities in policy incentive strength. This study develops a tripartite evolutionary game model encompassing government, production enterprises, and technology suppliers to systematically [...] Read more.
The digital economy presents multiple challenges to the promotion of green technologies, including behavioral uncertainty among firms, heterogeneous technological choices, and disparities in policy incentive strength. This study develops a tripartite evolutionary game model encompassing government, production enterprises, and technology suppliers to systematically explore the strategic evolution mechanisms underlying green technology adoption. A three-dimensional nonlinear dynamic system is constructed using replicator dynamics, and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is applied to identify key cost and benefit parameters for firms. Simulation results exhibit a strong match between the estimated parameters and simulated data, highlighting the model’s identifiability and explanatory capacity. In addition, the stability of eight pure strategy equilibrium points is examined through Jacobian analysis, revealing the evolutionary trajectories and local stability features across various strategic configurations. These findings offer theoretical guidance for optimizing green policy design and identifying behavioral pathways, while establishing a foundation for data-driven modeling of dynamic evolutionary processes. Full article
(This article belongs to the Special Issue Dynamic Analysis and Decision-Making in Complex Networks)
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