Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.

In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate...

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Autores principales: Casey Doyle, Thushara Gunda, Asmeret Naugle
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/0f1f6263e5de4819a88a9cc6259cbfe7
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spelling oai:doaj.org-article:0f1f6263e5de4819a88a9cc6259cbfe72021-12-02T20:10:52ZHierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.1932-620310.1371/journal.pone.0252266https://doaj.org/article/0f1f6263e5de4819a88a9cc6259cbfe72021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252266https://doaj.org/toc/1932-6203In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.Casey DoyleThushara GundaAsmeret NauglePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252266 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Casey Doyle
Thushara Gunda
Asmeret Naugle
Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.
description In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.
format article
author Casey Doyle
Thushara Gunda
Asmeret Naugle
author_facet Casey Doyle
Thushara Gunda
Asmeret Naugle
author_sort Casey Doyle
title Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.
title_short Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.
title_full Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.
title_fullStr Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.
title_full_unstemmed Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.
title_sort hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/0f1f6263e5de4819a88a9cc6259cbfe7
work_keys_str_mv AT caseydoyle hierarchicaleffectsfacilitatespreadingprocessesonsyntheticandempiricalmultilayernetworks
AT thusharagunda hierarchicaleffectsfacilitatespreadingprocessesonsyntheticandempiricalmultilayernetworks
AT asmeretnaugle hierarchicaleffectsfacilitatespreadingprocessesonsyntheticandempiricalmultilayernetworks
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