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Indirect influence in social networks as an induced percolation phenomenon

Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical obs...

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Autores principales: Xie, Jiarong, Wang, Xiangrong, Feng, Ling, Zhao, Jin-Hua, Liu, Wenyuan, Moreno, Yamir, Hu, Yanqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892329/
https://www.ncbi.nlm.nih.gov/pubmed/35217599
http://dx.doi.org/10.1073/pnas.2100151119
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author Xie, Jiarong
Wang, Xiangrong
Feng, Ling
Zhao, Jin-Hua
Liu, Wenyuan
Moreno, Yamir
Hu, Yanqing
author_facet Xie, Jiarong
Wang, Xiangrong
Feng, Ling
Zhao, Jin-Hua
Liu, Wenyuan
Moreno, Yamir
Hu, Yanqing
author_sort Xie, Jiarong
collection PubMed
description Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.
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spelling pubmed-88923292022-08-25 Indirect influence in social networks as an induced percolation phenomenon Xie, Jiarong Wang, Xiangrong Feng, Ling Zhao, Jin-Hua Liu, Wenyuan Moreno, Yamir Hu, Yanqing Proc Natl Acad Sci U S A Physical Sciences Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions. National Academy of Sciences 2022-02-25 2022-03-01 /pmc/articles/PMC8892329/ /pubmed/35217599 http://dx.doi.org/10.1073/pnas.2100151119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Xie, Jiarong
Wang, Xiangrong
Feng, Ling
Zhao, Jin-Hua
Liu, Wenyuan
Moreno, Yamir
Hu, Yanqing
Indirect influence in social networks as an induced percolation phenomenon
title Indirect influence in social networks as an induced percolation phenomenon
title_full Indirect influence in social networks as an induced percolation phenomenon
title_fullStr Indirect influence in social networks as an induced percolation phenomenon
title_full_unstemmed Indirect influence in social networks as an induced percolation phenomenon
title_short Indirect influence in social networks as an induced percolation phenomenon
title_sort indirect influence in social networks as an induced percolation phenomenon
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892329/
https://www.ncbi.nlm.nih.gov/pubmed/35217599
http://dx.doi.org/10.1073/pnas.2100151119
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