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Entropy
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Fractal and Multifractal Analysis of Complex Networks II
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Fractal and Multifractal Analysis of Complex Networks II

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 7972

Special Issue Editor

Dr. Rafał Rak

E-Mail Website
Guest Editor
College of Natural Sciences, Institute of Physics, University of Rzeszow, 35-310 Rzeszów, Poland
Interests: econophysics; complex networks; multifractals; complex systems; time series analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are surrounded by complex systems. Interactions between particles of matter, social phenomena resulting from cooperation between billions of individuals, communication and cooperation, the activity of billions of neurons in the brains of living beings, the structure and dynamics of financial markets, or the structure of mountain ridges are just a few examples of real-world complexity. To a greater or lesser extent, these systems play a significant role in both our private and professional lives. Therefore, obtaining a full understanding of these systems, describing them, and predicting their future behaviors are very interesting scientific challenges.

One of the tools contributing to a more complete description of complex phenomena is the language of complex networks, which has been developing for more than two decades.

Complex networks are an approach to studying different real systems through graph-based representation, which allows their observation with different graph measures, such as, among others, degree distribution, clustering coefficient, betweenness or assortativity.

However, these measures are local in nature; thus, the global structures that compose the networks are hidden. To study these global structures in networks, the best approach seems to be multifractal analysis (MFA), which consists of the measurement of fractal dimensions in different scales of the network.

Today’s level of technology development allows for an empirical analysis of large-scale and complex dynamical networks generated by Nature, and enables the construction and verification of more sophisticated models.

Currently, we know many interesting properties of real complex networks. These include scale-free, small-world, and self-similarity, which are closely related to the (multi-)fractality of complex systems.

Fractal and (in general) multifractal analysis enables the identification and better understanding of the nonlinear properties, hierarchical structure, and spatial heterogeneity of both real-word and synthetic systems.

The challenge is therefore to build tools (methods) that reliably identify the possible multifractal nature of networks. Some algorithms have been proposed for MFA, for both weighted and unweighted complex networks, in the past few years. However, as it often happens in research, the issue of creating new methods and ideas, especially in this field, has certainly not been exhausted yet.

This Special Issue will accept original ideas in the form of unpublished original manuscripts focused on topics arising from the broadly understood field of the quantitative analysis of “complex networks”, particularly their multiscale nature.

Dr. Rafał Rak
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
  • fractal and multifractal analysis of complex networks
  • complexity
  • neural networks
  • sociophysics
  • quantitative linguistics
  • mountain and river networks
  • brain networks
  • data science
  • time series analysis
  • social systems
  • financial markets
  • epidemic spreading
  • econophysics

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Related Special Issue

Published Papers (4 papers)

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Research

24 pages, 4949 KiB  
Article
Assessment of Fractal Synchronization during an Epileptic Seizure
by Oleg Gorshkov and Hernando Ombao
Entropy 2024, 26(8), 666; https://doi.org/10.3390/e26080666 - 5 Aug 2024
Viewed by 1082
Abstract
In this paper, we define fractal synchronization (FS) based on the idea of stochastic synchronization and propose a mathematical apparatus for estimating FS. One major advantage of our proposed approach is that fractal synchronization makes it possible to estimate the [...] Read more.
In this paper, we define fractal synchronization (FS) based on the idea of stochastic synchronization and propose a mathematical apparatus for estimating FS. One major advantage of our proposed approach is that fractal synchronization makes it possible to estimate the aggregate strength of the connection on multiple time scales between two projections of the attractor, which are time series with a fractal structure. We believe that one of the promising uses of FS is the assessment of the interdependence of encephalograms. To demonstrate this approach in evaluating the cross-dependence between channels in a network of electroencephalograms, we evaluated the FS of encephalograms during an epileptic seizure. Fractal synchronization demonstrates the presence of desynchronization during an epileptic seizure. Full article
(This article belongs to the Special Issue Fractal and Multifractal Analysis of Complex Networks II)
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13 pages, 1125 KiB  
Article
A Dynamic Monitoring Method of Public Opinion Risk of Overseas Direct Investment—Based on Multifractal Situation Optimization
by Yong Li
Entropy 2023, 25(11), 1491; https://doi.org/10.3390/e25111491 - 28 Oct 2023
Viewed by 1354
Abstract
The negative public opinions and views on overseas direct investment (ODI) of a multinational enterprise (MNE) will damage the image of its brand and are likely to bring it serious economic and social losses. So, it is important for the MNE to understand [...] Read more.
The negative public opinions and views on overseas direct investment (ODI) of a multinational enterprise (MNE) will damage the image of its brand and are likely to bring it serious economic and social losses. So, it is important for the MNE to understand the formation and spread mechanism of public opinion risk (POR) in order to effectively respond to and guide the public opinion. This research proposed a multifractal-based situation optimization method to explore the POR evolution based on the media-based negative sentiment on China’s ODI. The sentiment measurement is obtained by a directed crawler for gathering the text of media reports corresponding to a certain ODI event using a URL knowledge base from the GDELT Event Database. Taking the public opinion crisis of the tax evasion incident of the local arm of China’s MNE in India as an example, the experiments show that this method could dynamically monitor the POR event in real-time and help MNE guide the effective control and benign evolution of public opinion of the event. Full article
(This article belongs to the Special Issue Fractal and Multifractal Analysis of Complex Networks II)
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18 pages, 3399 KiB  
Article
The Complexity Behavior of Big and Small Trading Orders in the Chinese Stock Market
by Yu Zhu and Wen Fang
Entropy 2023, 25(1), 102; https://doi.org/10.3390/e25010102 - 4 Jan 2023
Cited by 1 | Viewed by 1799
Abstract
The Chinese stock market exhibits many characteristics that deviate from the efficient market hypothesis and the trading volume contains a great deal of complexity information that the price cannot reflect. Do small or big orders drive trading volume? We studied the complex behavior [...] Read more.
The Chinese stock market exhibits many characteristics that deviate from the efficient market hypothesis and the trading volume contains a great deal of complexity information that the price cannot reflect. Do small or big orders drive trading volume? We studied the complex behavior of different orders from a microstructure perspective. We used ETF data of the CSI300, SSE50, and CSI500 indices and divided transactions into big and small orders. A multifractal detrended fluctuation analysis (MFDFA) method was used to study persistence. It was found that the persistence of small orders was stronger than that of big orders, which was caused by correlation with time. A multiscale composite complexity synchronization (MCCS) method was used to study the synchronization of orders and total volume. It was found that small orders drove selling-out transactions in the CSI300 market and that big orders drove selling-out transactions in the CSI500 market. Our findings are useful for understanding the microstructure of the trading volume in the Chinese market. Full article
(This article belongs to the Special Issue Fractal and Multifractal Analysis of Complex Networks II)
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14 pages, 4802 KiB  
Article
Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting
by Mohammed A. A. Al-qaness, Ahmed A. Ewees, Laith Abualigah, Ayman Mutahar AlRassas, Hung Vo Thanh and Mohamed Abd Elaziz
Entropy 2022, 24(11), 1674; https://doi.org/10.3390/e24111674 - 17 Nov 2022
Cited by 21 | Viewed by 2594
Abstract
The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is [...] Read more.
The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine–cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods. Full article
(This article belongs to the Special Issue Fractal and Multifractal Analysis of Complex Networks II)
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