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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Auteurs principaux:	Casey Doyle, Thushara Gunda, Asmeret Naugle
Format:	article
Langue:	EN
Publié:	Public Library of Science (PLoS) 2021
Sujets:	Medicine R Science Q
Accès en ligne:	https://doaj.org/article/0f1f6263e5de4819a88a9cc6259cbfe7
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id	oai: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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Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks.

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