Global and local community memberships for estimating spreading capability of nodes in social networks

Abstract The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of inter...

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Autores principales:	Simon Krukowski, Tobias Hecking
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Spreading Networks Link clustering Community structure Applied mathematics. Quantitative methods T57-57.97
Acceso en línea:	https://doaj.org/article/445d5192d4054562be5e85ad041406ba
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spelling	oai:doaj.org-article:445d5192d4054562be5e85ad041406ba2021-11-07T12:18:59ZGlobal and local community memberships for estimating spreading capability of nodes in social networks10.1007/s41109-021-00421-32364-8228https://doaj.org/article/445d5192d4054562be5e85ad041406ba2021-11-01T00:00:00Zhttps://doi.org/10.1007/s41109-021-00421-3https://doaj.org/toc/2364-8228Abstract The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski and Hecking, in: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo (eds) Complex networks & their applications IX. Springer, Cham, pp 408–419, 2021) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that—in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps).Simon KrukowskiTobias HeckingSpringerOpenarticleSpreadingNetworksLink clusteringCommunity structureApplied mathematics. Quantitative methodsT57-57.97ENApplied Network Science, Vol 6, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Spreading Networks Link clustering Community structure Applied mathematics. Quantitative methods T57-57.97
spellingShingle	Spreading Networks Link clustering Community structure Applied mathematics. Quantitative methods T57-57.97 Simon Krukowski Tobias Hecking Global and local community memberships for estimating spreading capability of nodes in social networks
description	Abstract The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski and Hecking, in: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo (eds) Complex networks & their applications IX. Springer, Cham, pp 408–419, 2021) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that—in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps).
format	article
author	Simon Krukowski Tobias Hecking
author_facet	Simon Krukowski Tobias Hecking
author_sort	Simon Krukowski
title	Global and local community memberships for estimating spreading capability of nodes in social networks
title_short	Global and local community memberships for estimating spreading capability of nodes in social networks
title_full	Global and local community memberships for estimating spreading capability of nodes in social networks
title_fullStr	Global and local community memberships for estimating spreading capability of nodes in social networks
title_full_unstemmed	Global and local community memberships for estimating spreading capability of nodes in social networks
title_sort	global and local community memberships for estimating spreading capability of nodes in social networks
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/445d5192d4054562be5e85ad041406ba
work_keys_str_mv	AT simonkrukowski globalandlocalcommunitymembershipsforestimatingspreadingcapabilityofnodesinsocialnetworks AT tobiashecking globalandlocalcommunitymembershipsforestimatingspreadingcapabilityofnodesinsocialnetworks
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Global and local community memberships for estimating spreading capability of nodes in social networks

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