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Entropy
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Structures and Dynamics of Economic Complex Networks
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Structures and Dynamics of Economic Complex Networks

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 18517

Special Issue Editor

Prof. Jaan Kalda

E-Mail Website
Guest Editor
Department of Cybernetics, School of Science at Tallinn University of Technology, Tallinn 12 618, Estonia
Interests: modeling of fractal structures; statistical topography of random surfaces; statistical analysis of intermittent timeseries (including financial); turbulent diffusion; nonlinear transport processes

Special Issue Information

Dear colleagues,

Network science has been one of the most rapidly developing fields of statistical physics and mathematics over the last few decades, with one of the focal points being complex and scale-free networks, and with applications covering a wide spectrum of disciplines: social sciences, economy, geography, geology, epidemiology, population dynamics, gene technology, linguistics, machine learning, etc. The COVID-19 pandemic has shown us once again that knowing the topology of networks can be vital in the most literal meaning of the word. This applies equally well to the contact networks of the population through which the virus spreads and to the economic networks the topology of which determines how severely the economy is affected.

It is well known by now that the complexity of scale-free networks can extend far beyond being simply scale-free—for instance, there are multifractal and multilayer networks. However, there are still many challenges regarding the topology of the networks, the dynamics of the networks and dynamics on the networks. Just one simple example: There are scaling laws in economy (cf. Pareto’s law), and there are scale-free economic networks; what is the relationship between these two phenomena, and is one of these driven by the other?

This Special Issue calls for new ideas and methods addressing the complexity of economic networks; comprehensive reviews will also be accepted. Possible topics range from empirical studies of network topologies to the dynamical modeling of evolving networks, and to dynamics triggered by such networks.

Prof. Jaan Kalda
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. Entropy is an international peer-reviewed open access monthly 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
  • scale invariance
  • multilayer networks
  • network dynamics
  • dynamics over networks
  • resilience
  • data science

Published Papers (7 papers)

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Research

11 pages, 450 KiB  
Article
Propagation of Bankruptcy Risk over Scale-Free Economic Networks
by Joseph Andria, Giacomo di Tollo and Jaan Kalda
Entropy 2022, 24(12), 1713; https://doi.org/10.3390/e24121713 - 24 Nov 2022
Viewed by 1527
Abstract
The propagation of bankruptcy-induced shocks across domestic and global economies is sometimes very dramatic; this phenomenon can be modelled as a dynamical process in economic networks. Economic networks are usually scale-free, and scale-free networks are known to be vulnerable with respect to targeted [...] Read more.
The propagation of bankruptcy-induced shocks across domestic and global economies is sometimes very dramatic; this phenomenon can be modelled as a dynamical process in economic networks. Economic networks are usually scale-free, and scale-free networks are known to be vulnerable with respect to targeted attacks, i.e., attacks directed towards the biggest nodes of the network. Here we address the following question: to what extent does the scale-free nature of economic networks and the vulnerability of the biggest nodes affect the propagation of economic shocks? We model the dynamics of bankruptcies as the propagation of financial contagion across the banking sector over a scale-free network of banks, and perform Monte-Carlo simulations based on synthetic networks. In addition, we analyze the public data regarding the bankruptcy of US banks from the Federal Deposit Insurance Corporation. The dynamics of the shock propagation is characterized in terms of the Bank Failures Diffusion Index, i.e., the average number of new bankruptcies triggered by the bankruptcy of a single bank, and in terms of the Shannon entropy of the whole network. The simulation results are in-line with the empirical findings, and indicate the important role of the biggest banks in the dynamics of economic shocks. Full article
(This article belongs to the Special Issue Structures and Dynamics of Economic Complex Networks)
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19 pages, 2255 KiB  
Article
RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network
by Xiaoteng Yang, Zhenqiang Wu and Shumaila Javaid
Entropy 2022, 24(5), 702; https://doi.org/10.3390/e24050702 - 16 May 2022
Cited by 2 | Viewed by 1691 | Retraction
Abstract
The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the [...] Read more.
The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. When the preference of nodes in the network for higher ranks exceeds a certain threshold value, the equality state in the network becomes unstable and rank states emerge. Meanwhile, we select four real data sets for model evaluation and observe the resilience in the network hierarchy evolution and the differences formed by different patterns of hierarchy preference mechanisms, which help us better understand data science and network dynamics evolution. Full article
(This article belongs to the Special Issue Structures and Dynamics of Economic Complex Networks)
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16 pages, 15526 KiB  
Article
Do Co-Worker Networks Increase or Decrease Productivity Differences?
by László Lőrincz
Entropy 2021, 23(11), 1451; https://doi.org/10.3390/e23111451 - 31 Oct 2021
Cited by 1 | Viewed by 2029
Abstract
Do labor mobility and co-worker networks contribute to convergence or divergence between regions? Based on the previous literature, labor mobility contributes to knowledge transfer between firms. Therefore, mobility may contribute to decreasing productivity differences, while limited mobility sustains higher differences. The effect of [...] Read more.
Do labor mobility and co-worker networks contribute to convergence or divergence between regions? Based on the previous literature, labor mobility contributes to knowledge transfer between firms. Therefore, mobility may contribute to decreasing productivity differences, while limited mobility sustains higher differences. The effect of co-worker networks, however, can be two-fold in this process; they transmit information about potential jobs, which may enhance the mobility of workers—even between regions—and this enhanced mobility may contribute to levelling of differences. However, if mobility between regions involves movement costs, co-worker networks may concentrate locally—possibly contributing to the persistence of regional differences. In this paper, we build an agent-based model of labor mobility across firms and regions with knowledge spillovers that reflects key empirical observations on labor markets. We analyze the impact of network information provided about potential employers in this model and find that it contributes to increasing inter-regional mobility, and subsequently, to decreasing regional differences. We also find that both the density of coworker networks, as well as their regional concentrations, decrease if network information is available. Full article
(This article belongs to the Special Issue Structures and Dynamics of Economic Complex Networks)
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26 pages, 3598 KiB  
Article
How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach
by Jung-Bin Su
Entropy 2021, 23(9), 1151; https://doi.org/10.3390/e23091151 - 01 Sep 2021
Cited by 5 | Viewed by 1928
Abstract
This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage effect and non-normal return distribution can promote [...] Read more.
This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage effect and non-normal return distribution can promote the performance of volatility forecasting, and which one of the sixteen models possesses the best volatility forecasting performance. The eight parametric volatility forecasts models are composed of the generalized autoregressive conditional heteroskedasticity (GARCH) or GJR-GARCH volatility specification combining with the normal, Student’s t, skewed Student’s t, and generalized skewed Student’s t distributions. Empirical results show that, the performance for the composed volatility forecasting approach is significantly superior to that for the parametric volatility forecasting approach. Furthermore, the GJR-GARCH volatility specification has better performance than the GARCH one. In addition, the non-normal distribution does not have better forecasting performance than the normal distribution. In addition, the GJR-GARCH model combined with both the normal distribution and a neural network approach has the best performance of volatility forecasting among sixteen models. Thus, a neural network approach significantly promotes the performance of volatility forecasting. On the other hand, the setting of leverage effect can encourage the performance of volatility forecasting whereas the setting of non-normal distribution cannot. Full article
(This article belongs to the Special Issue Structures and Dynamics of Economic Complex Networks)
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19 pages, 3745 KiB  
Article
Research on Financial Systemic Risk in ASEAN Region
by Hong Fan and Renyun Liu
Entropy 2021, 23(9), 1131; https://doi.org/10.3390/e23091131 - 31 Aug 2021
Cited by 4 | Viewed by 1971
Abstract
The research of financial systemic risk is an important issue, however the research on the financial systemic risk in ASEAN region lacks. This paper uses the minimum density method to calculate the interbank network of ASEAN countries and uses the node centrality to [...] Read more.
The research of financial systemic risk is an important issue, however the research on the financial systemic risk in ASEAN region lacks. This paper uses the minimum density method to calculate the interbank network of ASEAN countries and uses the node centrality to judge the systemically important banks of various countries. Then the DebtRank algorithm is constructed to calculate the systemic risk value based on the interbank network. By comparing the systemic risk values obtained through the initial impact on the system important banks and non-important banks, we find that the systemic risk tends to reach the peak in the case of the initial impact on the system important banks. Furthermore, it is found that countries with high intermediary centrality and closeness centrality have higher systemic risk. It suggests that the regulatory authorities should implement legal supervision, strict supervision, and comprehensive supervision for key risk areas and weak links. Full article
(This article belongs to the Special Issue Structures and Dynamics of Economic Complex Networks)
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22 pages, 2352 KiB  
Article
Socio-Economic Impact of the Covid-19 Pandemic in the U.S.
by Jonathan Barlow and Irena Vodenska
Entropy 2021, 23(6), 673; https://doi.org/10.3390/e23060673 - 27 May 2021
Cited by 17 | Viewed by 5273
Abstract
This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a [...] Read more.
This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data. Full article
(This article belongs to the Special Issue Structures and Dynamics of Economic Complex Networks)
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22 pages, 5314 KiB  
Article
Structure of Investor Networks and Financial Crises
by Kęstutis Baltakys, Hung Le Viet and Juho Kanniainen
Entropy 2021, 23(4), 381; https://doi.org/10.3390/e23040381 - 24 Mar 2021
Cited by 3 | Viewed by 2202
Abstract
In this paper, we ask whether the structure of investor networks, estimated using shareholder registration data, is abnormal during a financial crises. We answer this question by analyzing the structure of investor networks through several most prominent global network features. The networks are [...] Read more.
In this paper, we ask whether the structure of investor networks, estimated using shareholder registration data, is abnormal during a financial crises. We answer this question by analyzing the structure of investor networks through several most prominent global network features. The networks are estimated from data on marketplace transactions of all publicly traded securities executed in the Helsinki Stock Exchange by Finnish stock shareholders between 1995 and 2016. We observe that most of the feature distributions were abnormal during the 2008–2009 financial crisis, with statistical significance. This paper provides evidence that the financial crisis was associated with a structural change in investors’ trade time synchronization. This indicates that the way how investors use their private information channels changes depending on the market conditions. Full article
(This article belongs to the Special Issue Structures and Dynamics of Economic Complex Networks)
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