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Selected Papers from the Ninth International Conference on Complex Networks and Their Applications

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 21665

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Prof. Dr. Hocine Cherifi

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Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB), UMR 6303, CNRS, University of Burgundy, 21078 Dijon, France
Interests: signal and image processing; computer vision; data compression; multimedia quality; complex networks
Special Issues, Collections and Topics in MDPI journals

Dr. Benjamin Renoust

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Institute for Datability Science, Osaka University, 1-3 Machikaneyamacho, Toyonaka, Osaka 560-0043, Japan
Interests: multimedia analytics; network analysis; complex networks; visual analytics; data analysis; information visualization

Special Issue Information

Dear Colleagues,

Since 2012, the International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS) has brought together researchers from different scientific communities working on areas related to Network Science. The ninth edition of this annual event will be held online from 1 to 3 December 2020. Selected contributions have been invited to be published in this Special Issue. They reflect the latest problems, advances, and diversity within the Network Science community.

Prof. Dr. Hocine Cherifi
Dr. Benjamin Renoust
Guest Editors

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

structural network measures
community structure
link analysis and ranking
motif discovery in complex networks
network models
diffusion and epidemics
temporal networks
multilayer networks
dynamics on/of networks
synchronization in networks
resilience and robustness of networks
controlling networks
reputation, influence, trust, mobility
networks in finance and economics
ecological networks and food webs
Earth sciences applications
biological networks
brain networks
urban systems and networks
network medicine
machine learning and networks

Related Special Issue

Advances in Complex Networks and Their Applications, from COMPLEX NETWORKS 2023 in Entropy (6 articles)

Published Papers (9 papers)

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Research

15 pages, 330 KiB

Open AccessArticle

Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks

by Felipe S. Abrahão, Klaus Wehmuth, Hector Zenil and Artur Ziviani

Entropy 2021, 23(7), 835; https://0-doi-org.brum.beds.ac.uk/10.3390/e23070835 - 29 Jun 2021

Cited by 3 | Viewed by 1542

Abstract

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

17 pages, 5953 KiB

Open AccessArticle

Local Fractal Connections to Characterize the Spatial Processes of Deforestation in the Ecuadorian Amazon

by Andrea Urgilez-Clavijo, David Andrés Rivas-Tabares, Juan José Martín-Sotoca and Ana María Tarquis Alfonso

Entropy 2021, 23(6), 748; https://0-doi-org.brum.beds.ac.uk/10.3390/e23060748 - 14 Jun 2021

Cited by 5 | Viewed by 2583

Abstract

Deforestation by human activities is a common issue in Amazonian countries. This occurs at different spatial and temporal scales causing primary forest loss and land fragmentation issues. During the deforestation process as the forest loses connectivity, the deforested patches create new intricate connections, which in turn create complex networks. In this study, we analyzed the local connected fractal dimension (LCFD) of the deforestation process in the Sumaco Biosphere Reserve (SBR) with two segmentation methods, —CA-wavelet and K-means—to categorize the complexity of deforested patches’ connections and then relate these with the spatial processes. The results showed an agreement with both methods, in which LCFD values below 1 corresponded to isolated patches with simple shapes and those above 1 signified more complex and connected patches. From CA-wavelet a threshold of 1.57 was detected allowing us to identify and discern low and high land transformation, while the threshold for K-means was 1.61. Both values represent the region from which deforestation performs local aggressive expansion networks. The thresholds were used to map the LCFD in which all spatial processes were visually detected. However, the threshold of 1.6 ± 0.03 was more effective in discerning high land transformation. such as shrinkage and attrition, in the deforestation process in the SBR. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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21 pages, 2708 KiB

Open AccessArticle

Improvement of Contact Tracing with Citizen’s Distributed Risk Maps

by Miguel Rebollo, Rosa María Benito, Juan Carlos Losada and Javier Galeano

Entropy 2021, 23(5), 638; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050638 - 20 May 2021

Cited by 1 | Viewed by 2260

Abstract

The rapid spread of COVID-19 has demonstrated the need for accurate information to contain its diffusion. Technological solutions are a complement that can help citizens to be informed about the risk in their environment. Although measures such as contact traceability have been successful in some countries, their use raises society’s resistance. This paper proposes a variation of the consensus processes in directed networks to create a risk map of a determined area. The process shares information with trusted contacts: people we would notify in the case of being infected. When the process converges, each participant would have obtained the risk map for the selected zone. The results are compared with the pilot project’s impact testing of the Spanish contact tracing app (RadarCOVID). The paper also depicts the results combining both strategies: contact tracing to detect potential infections and risk maps to avoid movements into conflictive areas. Although some works affirm that contact tracing apps need 60% of users to control the propagation, our results indicate that a 40% could be enough. On the other hand, the elaboration of risk maps could work with only 20% of active installations, but the effect is to delay the propagation instead of reducing the contagion. With both active strategies, this methodology is able to significantly reduce infected people with fewer participants. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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15 pages, 3680 KiB

Open AccessArticle

Impact of Coronavirus Outbreaks on Science and Society: Insights from Temporal Bibliometry of SARS and COVID-19

by Ramya Gupta, Abhishek Prasad, Suresh Babu and Gitanjali Yadav

Entropy 2021, 23(5), 626; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050626 - 18 May 2021

Cited by 3 | Viewed by 2737

Abstract

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both scientific and social standpoints. Despite several documented similarities, the coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20,000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. Unlike the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic to study changes in thematic foci and societal pressures dictating research over the course of a crisis. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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17 pages, 4742 KiB

Open AccessArticle

Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study

by Ernesto Sanz, Antonio Saa-Requejo, Carlos H. Díaz-Ambrona, Margarita Ruiz-Ramos, Alfredo Rodríguez, Eva Iglesias, Paloma Esteve, Bárbara Soriano and Ana M. Tarquis

Entropy 2021, 23(5), 576; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050576 - 07 May 2021

Cited by 7 | Viewed by 2674

Abstract

Estimates suggest that more than 70% of the world’s rangelands are degraded. The Normalized Difference Vegetation Index (NDVI) is commonly used by ecologists and agriculturalists to monitor vegetation and contribute to more sustainable rangeland management. This paper aims to explore the scaling character of NDVI and NDVI anomaly (NDVIa) time series by applying three fractal analyses: generalized structure function (GSF), multifractal detrended fluctuation analysis (MF-DFA), and Hurst index (HI). The study was conducted in four study areas in Southeastern Spain. Results suggest a multifractal character influenced by different land uses and spatial diversity. MF-DFA indicated an antipersistent character in study areas, while GSF and HI results indicated a persistent character. Different behaviors of generalized Hurst and scaling exponents were found between herbaceous and tree dominated areas. MF-DFA and surrogate and shuffle series allow us to study multifractal sources, reflecting the importance of long-range correlations in these areas. Two types of long-range correlation appear to be in place due to short-term memory reflecting seasonality and longer-term memory based on a time scale of a year or longer. The comparison of these series also provides us with a differentiating profile to distinguish among our four study areas that can improve land use and risk management in arid rangelands. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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26 pages, 4711 KiB

Open AccessArticle

The Vegetation–Climate System Complexity through Recurrence Analysis

by Andrés F. Almeida-Ñauñay, Rosa María Benito, Miguel Quemada, Juan Carlos Losada and Ana M. Tarquis

Entropy 2021, 23(5), 559; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050559 - 30 Apr 2021

Cited by 5 | Viewed by 2393

Abstract

Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year’s season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system’s complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil’s water balance that the VI reflects. Overall, we prove RP and CRP’s potential as adequate tools for analysing vegetation dynamics characterised by complexity. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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16 pages, 1221 KiB

Open AccessArticle

A Mixture Model of Truncated Zeta Distributions with Applications to Scientific Collaboration Networks

by Hohyun Jung and Frederick Kin Hing Phoa

Entropy 2021, 23(5), 502; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050502 - 22 Apr 2021

Cited by 2 | Viewed by 1853

Abstract

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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14 pages, 2106 KiB

Open AccessArticle

Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction

by Valentina Y. Guleva, Polina O. Andreeva and Danila A. Vaganov

Entropy 2021, 23(4), 492; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040492 - 20 Apr 2021

Viewed by 2002

Abstract

Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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12 pages, 1478 KiB

Open AccessArticle

Applying the Horizontal Visibility Graph Method to Study Irreversibility of Electromagnetic Turbulence in Non-Thermal Plasmas

by Belén Acosta-Tripailao, Denisse Pastén and Pablo S. Moya

Entropy 2021, 23(4), 470; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040470 - 16 Apr 2021

Cited by 14 | Viewed by 2529

Abstract

One of the fundamental open questions in plasma physics is the role of non-thermal particles distributions in poorly collisional plasma environments, a system that is commonly found throughout the Universe, e.g., the solar wind and the Earth’s magnetosphere correspond to natural plasma physics laboratories in which turbulent phenomena can be studied. Our study perspective is born from the method of Horizontal Visibility Graph (HVG) that has been developed in the last years to analyze time series avoiding the tedium and the high computational cost that other methods offer. Here, we build a complex network based on directed HVG technique applied to magnetic field fluctuations time series obtained from Particle In Cell (PIC) simulations of a magnetized collisionless plasma to distinguish the degree distributions and calculate the Kullback–Leibler Divergence (KLD) as a measure of relative entropy of data sets produced by processes that are not in equilibrium. First, we analyze the connectivity probability distribution for the undirected version of HVG finding how the Kappa distribution for low values of

κ

tends to be an uncorrelated time series, while the Maxwell–Boltzmann distribution shows a correlated stochastic processes behavior. Subsequently, we investigate the degree of temporary irreversibility of magnetic fluctuations that are self-generated by the plasma, comparing the case of a thermal plasma (described by a Maxwell–Botzmann velocity distribution function) with non-thermal Kappa distributions. We have shown that the KLD associated to the HVG is able to distinguish the level of reversibility that is associated to the thermal equilibrium in the plasma, because the dissipative degree of the system increases as the value of

κ

parameter decreases and the distribution function departs from the Maxwell–Boltzmann equilibrium. Full article

(This article belongs to the Special Issue Selected Papers from the Ninth International Conference on Complex Networks and Their Applications)

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