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Complex Networks and Interacting Particle Systems

Complex networks is a growing discipline aimed at understanding large interacting systems. One of its goals is to establish a relation between the interactions of a system and the networks structure that emerges. Taking a Lennard-Jones particle system as an example, we show that when interactions ar...

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Detalles Bibliográficos
Autores principales: Abadi, Noam, Ruzzenenti, Franco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670629/
https://www.ncbi.nlm.nih.gov/pubmed/37998182
http://dx.doi.org/10.3390/e25111490
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author Abadi, Noam
Ruzzenenti, Franco
author_facet Abadi, Noam
Ruzzenenti, Franco
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description Complex networks is a growing discipline aimed at understanding large interacting systems. One of its goals is to establish a relation between the interactions of a system and the networks structure that emerges. Taking a Lennard-Jones particle system as an example, we show that when interactions are governed by a potential, the notion of structure given by the physical arrangement of the interacting particles can be interpreted as a binary approximation to the interaction potential. This approximation simplifies the calculation of the partition function of the system and allows to study the stability of the interaction structure. We compare simulated results with those from the approximated partition function and show how the network and system perspective complement each other. With this, we draw a direct connection between the interactions of a molecular system and the network structure it forms and assess the degree to which it describes the system. We conclude by discussing the advantages and limitations of this method for weighted networks, as well as how this concept might be extended to more general systems.
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spelling pubmed-106706292023-10-27 Complex Networks and Interacting Particle Systems Abadi, Noam Ruzzenenti, Franco Entropy (Basel) Article Complex networks is a growing discipline aimed at understanding large interacting systems. One of its goals is to establish a relation between the interactions of a system and the networks structure that emerges. Taking a Lennard-Jones particle system as an example, we show that when interactions are governed by a potential, the notion of structure given by the physical arrangement of the interacting particles can be interpreted as a binary approximation to the interaction potential. This approximation simplifies the calculation of the partition function of the system and allows to study the stability of the interaction structure. We compare simulated results with those from the approximated partition function and show how the network and system perspective complement each other. With this, we draw a direct connection between the interactions of a molecular system and the network structure it forms and assess the degree to which it describes the system. We conclude by discussing the advantages and limitations of this method for weighted networks, as well as how this concept might be extended to more general systems. MDPI 2023-10-27 /pmc/articles/PMC10670629/ /pubmed/37998182 http://dx.doi.org/10.3390/e25111490 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abadi, Noam
Ruzzenenti, Franco
Complex Networks and Interacting Particle Systems
title Complex Networks and Interacting Particle Systems
title_full Complex Networks and Interacting Particle Systems
title_fullStr Complex Networks and Interacting Particle Systems
title_full_unstemmed Complex Networks and Interacting Particle Systems
title_short Complex Networks and Interacting Particle Systems
title_sort complex networks and interacting particle systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670629/
https://www.ncbi.nlm.nih.gov/pubmed/37998182
http://dx.doi.org/10.3390/e25111490
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