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Information diffusion in signed networks

Information diffusion has been widely discussed in various disciplines including sociology, economics, physics or computer science. In this paper, we generalize the linear threshold model in signed networks consisting of both positive and negative links. We analyze the dynamics of the spread of info...

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Detalles Bibliográficos
Autores principales: He, Xiaochen, Du, Haifeng, Feldman, Marcus W., Li, Guangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6818773/
https://www.ncbi.nlm.nih.gov/pubmed/31661504
http://dx.doi.org/10.1371/journal.pone.0224177
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author He, Xiaochen
Du, Haifeng
Feldman, Marcus W.
Li, Guangyu
author_facet He, Xiaochen
Du, Haifeng
Feldman, Marcus W.
Li, Guangyu
author_sort He, Xiaochen
collection PubMed
description Information diffusion has been widely discussed in various disciplines including sociology, economics, physics or computer science. In this paper, we generalize the linear threshold model in signed networks consisting of both positive and negative links. We analyze the dynamics of the spread of information based on balance theory, and find that a signed network can generate path dependence while structural balance can help remove the path dependence when seeded with balanced initialized active nodes. Simulation shows that the diffusion of information based on positive links contradicts that based on negative links. More positive links in signed networks are more likely to activate nodes and remove path dependence, but they can reduce predictability that is based on active states. We also find that a balanced structure can facilitate both the magnitude and speed of information diffusion, remove the path dependence, and cause polarization.
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spelling pubmed-68187732019-11-01 Information diffusion in signed networks He, Xiaochen Du, Haifeng Feldman, Marcus W. Li, Guangyu PLoS One Research Article Information diffusion has been widely discussed in various disciplines including sociology, economics, physics or computer science. In this paper, we generalize the linear threshold model in signed networks consisting of both positive and negative links. We analyze the dynamics of the spread of information based on balance theory, and find that a signed network can generate path dependence while structural balance can help remove the path dependence when seeded with balanced initialized active nodes. Simulation shows that the diffusion of information based on positive links contradicts that based on negative links. More positive links in signed networks are more likely to activate nodes and remove path dependence, but they can reduce predictability that is based on active states. We also find that a balanced structure can facilitate both the magnitude and speed of information diffusion, remove the path dependence, and cause polarization. Public Library of Science 2019-10-29 /pmc/articles/PMC6818773/ /pubmed/31661504 http://dx.doi.org/10.1371/journal.pone.0224177 Text en © 2019 He et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
He, Xiaochen
Du, Haifeng
Feldman, Marcus W.
Li, Guangyu
Information diffusion in signed networks
title Information diffusion in signed networks
title_full Information diffusion in signed networks
title_fullStr Information diffusion in signed networks
title_full_unstemmed Information diffusion in signed networks
title_short Information diffusion in signed networks
title_sort information diffusion in signed networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6818773/
https://www.ncbi.nlm.nih.gov/pubmed/31661504
http://dx.doi.org/10.1371/journal.pone.0224177
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