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Mathematics
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Complex Network Modeling: Theory and Applications, 2nd Edition
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Complex Network Modeling: Theory and Applications, 2nd Edition

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3215

Special Issue Editor

Dr. Gaogao Dong

E-Mail Website
Guest Editor
School of Mathematical Sciences, Jiangsu University, Zhenjiang 212013, China
Interests: network science; complex systems; statistical physics; complex system; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of information technology, the study of complex networks has become increasingly important and attracted researchers in different fields. Complex network theory, by correlating disorganized information, allows people to quantify and predict the real-world systems accurately. Although a great deal of research has been conducted on complex networks to date, it is still under-researched for various reasons, including the rapid development of science and technology and the explosion of big data. This Special Issue aims to investigate the theory of complex networks, modelling by use of complex networks, and the application of complex networks to multidisciplinary fields.

Submissions of manuscripts on complex network modeling, structure and function analysis, percolation theory; modelling, structural and functional analysis of complex networks; dynamical analysis on complex networks; network control, control and stability of multi-intelligent systems; biological networks, systems biology, biodynamic systems; network analysis of social, economic and technological networks; basic theory and applications of cyber security; complex networks and big data analysis and computation; the intersection of complex systems with other disciplines and their applications, etc. are welcome.

The Special Issue will bring together contributions from researchers in nonlinear dynamics, statistical physics, systems science, computer science, social psychology, communication, and other scientific fields. Papers describing the theoretical studies of principles, as well as new experimental results, are expected.

Dr. Gaogao Dong
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 network modeling, structure, and function analysis
  • network analysis of social, economic, and technological networks
  • dynamics on complex networks: propagation, games
  • complex networks and big data analytics and computing
  • network security fundamental theory and application
  • complex network applications: link prediction and recommendation algorithms

Related Special Issue

Published Papers (3 papers)

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Research

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24 pages, 5916 KiB  
Article
Portfolio Construction: A Network Approach
by Evangelos Ioannidis, Iordanis Sarikeisoglou and Georgios Angelidis
Mathematics 2023, 11(22), 4670; https://doi.org/10.3390/math11224670 - 16 Nov 2023
Cited by 1 | Viewed by 1414
Abstract
A key parameter when investing is Time Horizon. One of the biggest mistakes investors make is not aligning the timeline of their goals with their investment portfolio. In other words, time horizons determine the investment portfolio you should construct. We examine which [...] Read more.
A key parameter when investing is Time Horizon. One of the biggest mistakes investors make is not aligning the timeline of their goals with their investment portfolio. In other words, time horizons determine the investment portfolio you should construct. We examine which portfolios are the best for long-term investing, short-term investing, and intraday trading. This study presents a novel approach for portfolio construction based on Network Science. We use daily returns of stocks that compose the Dow Jones Industrial Average (DJIA) for a 25-year period from 1998 to 2022. Stock networks are estimated from (i) Pearson correlation (undirected linear statistical correlations), as well as (ii) Transfer Entropy (directed non-linear causal relationships). Portfolios are constructed in two main ways: (a) only four stocks are selected, depending on their centrality, with Markowitz investing weights, or (b) all stocks are selected with centrality-based investing weights. Portfolio performance is evaluated in terms of the following indicators: return, risk (total and systematic), and risk-adjusted return (Sharpe ratio and Treynor ratio). Results are compared against two benchmarks: the index DJIA, and the Markowitz portfolio based on Modern Portfolio Theory. The key findings are as follows: (1) Peripheral portfolios of low centrality stocks based on Pearson correlation network are the best in the long-term, achieving an extremely high cumulative return of around 3000% as well as high risk-adjusted return; (2) Markowitz portfolio is the safest in the long-term, while on the contrary, central portfolios of high centrality stocks based on Pearson correlation network are the riskiest; (3) In times of crisis, no portfolio is always the best. However, portfolios based on Transfer Entropy network perform better in most of the crises; (4) Portfolios of all stocks selected with centrality-based investing weights outperform in both short-term investing and intraday trading. A stock brokerage company may utilize the above findings of our work to enhance its portfolio management services. Full article
(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications, 2nd Edition)
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15 pages, 4185 KiB  
Article
Outer Synchronization of Two Muti-Layer Dynamical Complex Networks with Intermittent Pinning Control
by Yi Liang, Yunyun Deng and Chuan Zhang
Mathematics 2023, 11(16), 3543; https://doi.org/10.3390/math11163543 - 16 Aug 2023
Cited by 4 | Viewed by 747
Abstract
This paper regards the outer synchronization of multi-layer dynamical networks with additive couplings via aperiodically intermittent pinning control, in which different layers of each multi-layer network have different topological structures. First, a state-feedback intermittent pinning controller is designed in the drive and response [...] Read more.
This paper regards the outer synchronization of multi-layer dynamical networks with additive couplings via aperiodically intermittent pinning control, in which different layers of each multi-layer network have different topological structures. First, a state-feedback intermittent pinning controller is designed in the drive and response configuration, and sufficient conditions to achieve the outer synchronization are derived based on the Lyapunov stability theory and matrix inequalities. Second, outer synchronization problem of multi-layer networks is discussed by setting an adaptive intermittent pinning controller; an appropriate Lyapunov function is selected to prove the criteria of synchronization between the drive multi-layer network and the response multi-layer network. Finally, three simulation examples are given to show the effectiveness of our control schemes. Full article
(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications, 2nd Edition)
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Review

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27 pages, 456 KiB  
Review
Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business
by Dimitris Tsintsaris, Milan Tsompanoglou and Evangelos Ioannidis
Mathematics 2024, 12(8), 1141; https://doi.org/10.3390/math12081141 - 10 Apr 2024
Viewed by 692
Abstract
In this paper we offer a comprehensive review of Sociophysics, focusing on relevant models as well as selected applications in social trading, behavioral finance and business. We discuss three key aspects of social diffusion dynamics, namely Opinion Dynamics (OD), Group Decision-Making (GDM) and [...] Read more.
In this paper we offer a comprehensive review of Sociophysics, focusing on relevant models as well as selected applications in social trading, behavioral finance and business. We discuss three key aspects of social diffusion dynamics, namely Opinion Dynamics (OD), Group Decision-Making (GDM) and Knowledge Dynamics (KD). In the OD case, we highlight special classes of social agents, such as informed agents, contrarians and extremists. As regards GDM, we present state-of-the-art models on various kinds of decision-making processes. In the KD case, we discuss processes of knowledge diffusion and creation via the presence of self-innovating agents. The primary question we wish to address is: to what extent does Sociophysics correspond to social reality? For that purpose, for each social diffusion model category, we present notable Sociophysics applications for real-world socioeconomic phenomena and, additionally, we provide a much-needed critique of the existing Sociophysics literature, so as to raise awareness of certain issues that currently undermine the effective application of Sociophysics, mainly in terms of modelling assumptions and mathematical formulation, on the investigation of key social processes. Full article
(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications, 2nd Edition)
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