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Selected Papers from the Tenth International Conference on Complex Networks & 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 2022) | Viewed by 19467

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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

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 Tenth Edition of this annual event will be held in a hybrid format from 30 November to 2 December 2021. Selected contributions will be invited for submission to this Special Issue. They reflect the latest problems, advances, and diversity within the network science community.

Prof. Dr. Hocine Cherifi
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

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, and trust
mobility
networks in finance and economics
ecological networks and food webs
earth science 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 (11 papers)

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Research

14 pages, 904 KiB

Open AccessArticle

MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering

by Ahlem Drif and Hocine Cherifi

Entropy 2022, 24(8), 1084; https://doi.org/10.3390/e24081084 - 05 Aug 2022

Cited by 8 | Viewed by 1713

Abstract

Many web platforms now include recommender systems. Network representation learning has been a successful approach for building these efficient recommender systems. However, learning the mutual influence of nodes in the network is challenging. Indeed, it carries collaborative signals accounting for complex user-item interactions on user decisions. For this purpose, in this paper, we develop a Mutual Interaction Graph Attention Network “MIGAN”, a new algorithm based on self-supervised representation learning on a large-scale bipartite graph (BGNN). Experimental investigation with real-world data demonstrates that MIGAN compares favorably with the baselines in terms of prediction accuracy and recommendation efficiency. Full article

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

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12 pages, 5618 KiB

Open AccessArticle

Assigning Degrees of Stochasticity to Blazar Light Curves in the Radio Band Using Complex Networks

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

Entropy 2022, 24(8), 1063; https://doi.org/10.3390/e24081063 - 02 Aug 2022

Cited by 3 | Viewed by 1626

Abstract

We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allows us to obtain properties from degree distributions, such as a characteristic exponent to describe its stochasticity and the Kullback–Leibler Divergence (KLD), presenting a new perspective to the methods commonly used to study Active Galactic Nuclei (AGN). We contrast these parameters with the excess variance, which is an astronomical measurement of variability in light curves; at the same time, we use the spectral classification of the sources. While it is not possible to find significant correlations with the excess variance, the degree distributions extracted from the network are detecting differences related to the spectral classification of blazars. These differences suggest a chaotic behavior in the time series for the BL Lac sources and a correlated stochastic behavior in the time series for the FSRQ sources. Our results show that complex networks may be a valuable alternative tool to study AGNs according to the variability of their energy output. Full article

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

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28 pages, 1724 KiB

Open AccessArticle

The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets

by Francesco Maria De Collibus, Matija Piškorec, Alberto Partida and Claudio J. Tessone

Entropy 2022, 24(8), 1048; https://doi.org/10.3390/e24081048 - 30 Jul 2022

Cited by 5 | Viewed by 2114

Abstract

In this paper, we use the methods of networks science to analyse the transaction networks of tokens running on the Ethereum blockchain. We start with a deep dive on four of them: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We study two types of blockchain addresses, smart contracts (SC), which run code, and externally owned accounts (EOA), run by human users, or off-chain code, with the corresponding private keys. We use preferential attachment and network dismantling strategies to evaluate their importance for the network structure. Subsequently, we expand our view to all ERC-20 tokens issued on the Ethereum network. We first study multilayered networks composed of Ether (ETH) and individual tokens using a dismantling approach to assess how the deconstruction starting from one network affects the other. Finally, we analyse the Ether network and Ethereum-based token networks to find similarities between sets of high-degree nodes. For this purpose, we use both the traditional Jaccard Index and a new metric that we introduce, the Ordered Jaccard Index (OJI), which considers the order of the elements in the two sets that are compared. Our findings suggest that smart contracts and exchange-related addresses play a structural role in transaction networks both in DeFi and Ethereum. The presence in the network of nodes associated to addresses of smart contracts and exchanges is positively correlated with the success of the token network measured in terms of network size and market capitalisation. These nodes play a fundamental role in the centralisation of the supposedly decentralised finance (DeFi) ecosystem: without them, their networks would quickly collapse. Full article

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

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17 pages, 1124 KiB

Open AccessArticle

Complex Network Analysis of Mass Violation, Specifically Mass Killing

by Iqra Erum, Rauf Ahmed Shams Malick, Ghufran Ahmed and Hocine Cherifi

Entropy 2022, 24(8), 1017; https://doi.org/10.3390/e24081017 - 23 Jul 2022

Viewed by 1478

Abstract

News reports in media contain news about society’s social and political conditions. With the help of publicly available digital datasets of events, it is possible to study a complex network of mass violations, i.e., Mass Killings. Multiple approaches have been applied to bring essential insights into the events and involved actors. Power law distribution behavior finds in the tail of actor mention, co-actor mention, and actor degree tells us about the dominant behavior of influential actors that grows their network with time. The United States, France, Israel, and a few other countries have been identified as major players in the propagation of Mass Killing throughout the past 20 years. It is demonstrated that targeting the removal of influential actors may stop the spreading of such conflicting events and help policymakers and organizations. This paper aims to identify and formulate the conflicts with the actor’s perspective at a global level for a period of time. This process is a generalization to be applied to any level of news, i.e., it is not restricted to only the global level. Full article

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

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25 pages, 534 KiB

Open AccessFeature PaperArticle

Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata

by Larissa Mori, Kaleigh O’Hara, Toyya A. Pujol and Mario Ventresca

Entropy 2022, 24(6), 842; https://doi.org/10.3390/e24060842 - 18 Jun 2022

Cited by 3 | Viewed by 1818

Abstract

With the goal of understanding if the information contained in node metadata can help in the task of link weight prediction, we investigate herein whether incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link improves the prediction accuracy of common supervised machine learning methods. In contrast with previous works, instead of normalizing the link weights, we treat them as count variables representing the number of interactions between end nodes, as this is a natural representation for many datasets in the literature. In this preliminary study, we find no significant evidence that metadata similarity improved the prediction accuracy of the four empirical datasets studied. To further explore the role of node metadata in weight prediction, we synthesized weights to analyze the extreme case where the weights depend solely on the metadata of the end nodes, while encoding different relationships between them using logical operators in the generation process. Under these conditions, the random forest method performed significantly better than other methods in 99.07% of cases, though the prediction accuracy was significantly degraded for the methods analyzed in comparison to the experiments with the original weights. Full article

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

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17 pages, 1430 KiB

Open AccessArticle

Complex Network Study of Solar Magnetograms

by Víctor Muñoz and Eduardo Flández

Entropy 2022, 24(6), 753; https://doi.org/10.3390/e24060753 - 26 May 2022

Cited by 3 | Viewed by 1731

Abstract

In this paper, we study solar magnetic activity by means of a complex network approach. A complex network was built based on information on the space and time evolution of sunspots provided by image recognition algorithms on solar magnetograms taken during the complete 23rd solar cycle. Both directed and undirected networks were built, and various measures such as degree distributions, clustering coefficient, average shortest path, various centrality measures, and Gini coefficients calculated for all them. We find that certain measures are correlated with solar activity and others are anticorrelated, while several measures are essentially constant along the solar cycle. Thus, we show that complex network analysis can yield useful information on the evolution of solar activity and reveal universal features valid at any stage of the solar cycle; the implications of this research for the prediction of solar maxima are discussed as well. Full article

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

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21 pages, 703 KiB

Open AccessFeature PaperArticle

Sensing Enhancement on Social Networks: The Role of Network Topology

by Markus Brede and Guillermo Romero-Moreno

Entropy 2022, 24(5), 738; https://doi.org/10.3390/e24050738 - 22 May 2022

Viewed by 1841

Abstract

Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that, whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and that random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small-world models, we find that sensing enhancement tends to be boosted in the small-world regime. Full article

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

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19 pages, 6485 KiB

Open AccessArticle

Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks

by Koduru Hajarathaiah, Murali Krishna Enduri, Satish Anamalamudi, Tatireddy Subba Reddy and Srilatha Tokala

Entropy 2022, 24(5), 704; https://doi.org/10.3390/e24050704 - 16 May 2022

Cited by 11 | Viewed by 2001

Abstract

Computing influential nodes gets a lot of attention from many researchers for information spreading in complex networks. It has vast applications, such as viral marketing, social leader creation, rumor control, and opinion monitoring. The information-spreading ability of influential nodes is greater compared with other nodes in the network. Several researchers proposed centrality measures to compute the influential nodes in a complex network, such as degree, betweenness, closeness, semi-local centralities, and PageRank. These centrality methods are defined based on the local and/or global information of nodes in the network. However, due to their high time complexity, centrality measures based on the global information of nodes have become unsuitable for large-scale networks. Very few centrality measures exist that are based on the attributes between nodes and the structure of the network. We propose the nearest neighborhood trust PageRank (NTPR) based on the structural attributes of neighbors and nearest neighbors of nodes. We define the measure based on the degree ratio, the similarity between nodes, the trust values of neighbors, and the nearest neighbors. We computed the influential nodes in various real-world networks using the proposed centrality method. We found the maximum influence by using influential nodes with SIR and independent cascade methods. We also compare the maximum influence of our centrality measure with the existing basic centrality measures. Full article

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

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25 pages, 1250 KiB

Open AccessArticle

Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents

by Zhongqi Cai, Enrico Gerding and Markus Brede

Entropy 2022, 24(5), 640; https://doi.org/10.3390/e24050640 - 02 May 2022

Cited by 1 | Viewed by 1343

Abstract

Using observational data to infer the coupling structure or parameters in dynamical systems is important in many real-world applications. In this paper, we propose a framework of strategically influencing a dynamical process that generates observations with the aim of making hidden parameters more easily inferable. More specifically, we consider a model of networked agents who exchange opinions subject to voting dynamics. Agent dynamics are subject to peer influence and to the influence of two controllers. One of these controllers is treated as passive and we presume its influence is unknown. We then consider a scenario in which the other active controller attempts to infer the passive controller’s influence from observations. Moreover, we explore how the active controller can strategically deploy its own influence to manipulate the dynamics with the aim of accelerating the convergence of its estimates of the opponent. Along with benchmark cases we propose two heuristic algorithms for designing optimal influence allocations. We establish that the proposed algorithms accelerate the inference process by strategically interacting with the network dynamics. Investigating configurations in which optimal control is deployed. We first find that agents with higher degrees and larger opponent allocations are harder to predict. Second, even factoring in strategical allocations, opponent’s influence is typically the harder to predict the more degree-heterogeneous the social network. Full article

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

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18 pages, 344 KiB

Open AccessArticle

Community Partitioning over Feature-Rich Networks Using an Extended K-Means Method

by Soroosh Shalileh and Boris Mirkin

Entropy 2022, 24(5), 626; https://doi.org/10.3390/e24050626 - 29 Apr 2022

Cited by 4 | Viewed by 1340

Abstract

This paper proposes a meaningful and effective extension of the celebrated K-means algorithm to detect communities in feature-rich networks, due to our assumption of non-summability mode. We least-squares approximate given matrices of inter-node links and feature values, leading to a straightforward extension of the conventional K-means clustering method as an alternating minimization strategy for the criterion. This works in a two-fold space, embracing both the network nodes and features. The metric used is a weighted sum of the squared Euclidean distances in the feature and network spaces. To tackle the so-called curse of dimensionality, we extend this to a version that uses the cosine distances between entities and centers. One more version of our method is based on the Manhattan distance metric. We conduct computational experiments to test our method and compare its performances with those by competing popular algorithms at synthetic and real-world datasets. The cosine-based version of the extended K-means typically wins at the high-dimension real-world datasets. In contrast, the Manhattan-based version wins at most synthetic datasets. Full article

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

15 pages, 5316 KiB

Open AccessArticle

A Multi-Scale Entropy Approach to Study Collapse and Anomalous Diffusion in Shared Mobility Systems

by Francisco Prieto-Castrillo, Javier Borondo, Rubén Martín García and Rosa M. Benito

Entropy 2022, 24(5), 606; https://doi.org/10.3390/e24050606 - 27 Apr 2022

Cited by 1 | Viewed by 1453

Abstract

In this paper, we study the phenomena of collapse and anomalous diffusion in shared mobility systems. In particular, we focus on a fleet of vehicles moving through a stations network and analyse the effect of self-journeys in system stability, using a mathematical simplex under stochastic flows. With a birth-death process approach, we find analytical upper bounds for random walk and we monitor how the system collapses by super diffusing under different randomization conditions. Using the multi-scale entropy metric, we show that real data from a bike-sharing fleet in the city of Salamanca (Spain) present a complex behaviour with more of a

1 / f

signal than a disorganized system with a white noise signal. Full article

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

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