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Entropy
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Selected Papers from the 11th International Conference on Complex Networks...
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Selected Papers from the 11th International Conference on Complex Networks and Their Applications

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 6218

Special Issue Editors

Prof. Dr. Hocine Cherifi

E-Mail Website
Guest Editor
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
Prof. Dr. Sabrina Gaito

E-Mail Website
Guest Editor
Department of Computer Science, Universita degli Studi di Milano, I-20135 Milan, Italy
Interests: delay and opportunistic networks; edge computing; smart cities; human mobility; social media mining and network science
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 eleventh edition of this annual event will be held in a hybrid format from 8 to 10 November 2022. 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
Prof. Dr. Sabrina Gaito
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, 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

Published Papers (4 papers)

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Research

17 pages, 2405 KiB  
Article
Graph Partitions in Chemistry
by Ioannis Michos and Vasilios Raptis
Entropy 2023, 25(11), 1504; https://doi.org/10.3390/e25111504 - 31 Oct 2023
Viewed by 1454
Abstract
We study partitions (equitable, externally equitable, or other) of graphs that describe physico-chemical systems at the atomic or molecular level; provide examples that show how these partitions are intimately related with symmetries of the systems; and discuss how such a link can further [...] Read more.
We study partitions (equitable, externally equitable, or other) of graphs that describe physico-chemical systems at the atomic or molecular level; provide examples that show how these partitions are intimately related with symmetries of the systems; and discuss how such a link can further lead to insightful relations with the systems’ physical and chemical properties. We define a particular kind of graph partition, which we call Chemical Equitable Partition (CEP), accounting for chemical composition as well as connectivity and associate it with a quantitative measure of information reduction that accompanies its derivation. These concepts are applied to model molecular and crystalline solid systems, illustrating their potential as a means to classify atoms according to their chemical or crystallographic role. We also cluster materials in meaningful manners that take their microstructure into account and even correlate them with the materials’ physical properties. Full article
(This article belongs to the Special Issue Selected Papers from the 11th International Conference on Complex Networks and Their Applications)
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30 pages, 983 KiB  
Article
A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks
by Francesco Zito, Vincenzo Cutello and Mario Pavone
Entropy 2023, 25(8), 1214; https://doi.org/10.3390/e25081214 - 15 Aug 2023
Cited by 2 | Viewed by 2329
Abstract
The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention [...] Read more.
The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research. By simulating gene expression, we can gain insights into the complex mechanisms that control gene expression and how they are affected by various environmental factors. This knowledge can be used to develop new treatments for genetic diseases, improve crop yields, and better understand the evolution of species. In this article, we address this issue by focusing on a novel method capable of simulating the gene expression regulation of a group of genes and their mutual interactions. Our framework enables us to simulate the regulation of gene expression in response to alterations or perturbations that can affect the expression of a gene. We use both artificial and real benchmarks to empirically evaluate the effectiveness of our methodology. Furthermore, we compare our method with existing ones to understand its advantages and disadvantages. We also present future ideas for improvement to enhance the effectiveness of our method. Overall, our approach has the potential to greatly improve the field of gene expression simulation and gene regulatory network inference, possibly leading to significant advancements in genetics. Full article
(This article belongs to the Special Issue Selected Papers from the 11th International Conference on Complex Networks and Their Applications)
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20 pages, 1594 KiB  
Article
Centrality Learning: Auralization and Route Fitting
by Xin Li, Liav Bachar and Rami Puzis
Entropy 2023, 25(8), 1115; https://doi.org/10.3390/e25081115 - 26 Jul 2023
Viewed by 764
Abstract
Developing a tailor-made centrality measure for a given task requires domain- and network-analysis expertise, as well as time and effort. Thus, automatically learning arbitrary centrality measures for providing ground-truth node scores is an important research direction. We propose a generic deep-learning architecture for [...] Read more.
Developing a tailor-made centrality measure for a given task requires domain- and network-analysis expertise, as well as time and effort. Thus, automatically learning arbitrary centrality measures for providing ground-truth node scores is an important research direction. We propose a generic deep-learning architecture for centrality learning which relies on two insights: 1. Arbitrary centrality measures can be computed using Routing Betweenness Centrality (RBC); 2. As suggested by spectral graph theory, the sound emitted by nodes within the resonating chamber formed by a graph represents both the structure of the graph and the location of the nodes. Based on these insights and our new differentiable implementation of Routing Betweenness Centrality (RBC), we learn routing policies that approximate arbitrary centrality measures on various network topologies. Results show that the proposed architecture can learn multiple types of centrality indices more accurately than the state of the art. Full article
(This article belongs to the Special Issue Selected Papers from the 11th International Conference on Complex Networks and Their Applications)
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20 pages, 1323 KiB  
Article
Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree
by Fenghua Wang and Robert E. Kooij
Entropy 2023, 25(4), 656; https://doi.org/10.3390/e25040656 - 14 Apr 2023
Viewed by 942
Abstract
Network controllability and its robustness have been widely studied. However, analytical methods to calculate network controllability with respect to node in- and out-degree targeted removals are currently lacking. This paper develops methods, based on generating functions for the in- and out-degree distributions, to [...] Read more.
Network controllability and its robustness have been widely studied. However, analytical methods to calculate network controllability with respect to node in- and out-degree targeted removals are currently lacking. This paper develops methods, based on generating functions for the in- and out-degree distributions, to approximate the minimum number of driver nodes needed to control directed networks, during node in- and out-degree targeted removals. By validating the proposed methods on synthetic and real-world networks, we show that our methods work reasonably well. Moreover, when the fraction of the removed nodes is below 10% the analytical results of random removals can also be used to predict the results of targeted node removals. Full article
(This article belongs to the Special Issue Selected Papers from the 11th International Conference on Complex Networks and Their Applications)
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