On the robustness of in- and out-components in a temporal network.

<h4>Background</h4>Many networks exhibit time-dependent topologies, where an edge only exists during a certain period of time. The first measurements of such networks are very recent so that a profound theoretical understanding is still lacking. In this work, we focus on the propagation...

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Autores principales:	Mario Konschake, Hartmut H K Lentz, Franz J Conraths, Philipp Hövel, Thomas Selhorst
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d754067735c74a8bb57823e7cecf54f9
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spelling	oai:doaj.org-article:d754067735c74a8bb57823e7cecf54f92021-11-18T07:58:39ZOn the robustness of in- and out-components in a temporal network.1932-620310.1371/journal.pone.0055223https://doaj.org/article/d754067735c74a8bb57823e7cecf54f92013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23405124/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Many networks exhibit time-dependent topologies, where an edge only exists during a certain period of time. The first measurements of such networks are very recent so that a profound theoretical understanding is still lacking. In this work, we focus on the propagation properties of infectious diseases in time-dependent networks. In particular, we analyze a dataset containing livestock trade movements. The corresponding networks are known to be a major route for the spread of animal diseases. In this context chronology is crucial. A disease can only spread if the temporal sequence of trade contacts forms a chain of causality. Therefore, the identification of relevant nodes under time-varying network topologies is of great interest for the implementation of counteractions.<h4>Methodology/findings</h4>We find that a time-aggregated approach might fail to identify epidemiologically relevant nodes. Hence, we explore the adaptability of the concept of centrality of nodes to temporal networks using a data-driven approach on the example of animal trade. We utilize the size of the in- and out-component of nodes as centrality measures. Both measures are refined to gain full awareness of the time-dependent topology and finite infectious periods. We show that the size of the components exhibit strong temporal heterogeneities. In particular, we find that the size of the components is overestimated in time-aggregated networks. For disease control, however, a risk assessment independent of time and specific disease properties is usually favored. We therefore explore the disease parameter range, in which a time-independent identification of central nodes remains possible.<h4>Conclusions</h4>We find a ranking of nodes according to their component sizes reasonably stable for a wide range of infectious periods. Samples based on this ranking are robust enough against varying disease parameters and hence are promising tools for disease control.Mario KonschakeHartmut H K LentzFranz J ConrathsPhilipp HövelThomas SelhorstPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e55223 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mario Konschake Hartmut H K Lentz Franz J Conraths Philipp Hövel Thomas Selhorst On the robustness of in- and out-components in a temporal network.
description	<h4>Background</h4>Many networks exhibit time-dependent topologies, where an edge only exists during a certain period of time. The first measurements of such networks are very recent so that a profound theoretical understanding is still lacking. In this work, we focus on the propagation properties of infectious diseases in time-dependent networks. In particular, we analyze a dataset containing livestock trade movements. The corresponding networks are known to be a major route for the spread of animal diseases. In this context chronology is crucial. A disease can only spread if the temporal sequence of trade contacts forms a chain of causality. Therefore, the identification of relevant nodes under time-varying network topologies is of great interest for the implementation of counteractions.<h4>Methodology/findings</h4>We find that a time-aggregated approach might fail to identify epidemiologically relevant nodes. Hence, we explore the adaptability of the concept of centrality of nodes to temporal networks using a data-driven approach on the example of animal trade. We utilize the size of the in- and out-component of nodes as centrality measures. Both measures are refined to gain full awareness of the time-dependent topology and finite infectious periods. We show that the size of the components exhibit strong temporal heterogeneities. In particular, we find that the size of the components is overestimated in time-aggregated networks. For disease control, however, a risk assessment independent of time and specific disease properties is usually favored. We therefore explore the disease parameter range, in which a time-independent identification of central nodes remains possible.<h4>Conclusions</h4>We find a ranking of nodes according to their component sizes reasonably stable for a wide range of infectious periods. Samples based on this ranking are robust enough against varying disease parameters and hence are promising tools for disease control.
format	article
author	Mario Konschake Hartmut H K Lentz Franz J Conraths Philipp Hövel Thomas Selhorst
author_facet	Mario Konschake Hartmut H K Lentz Franz J Conraths Philipp Hövel Thomas Selhorst
author_sort	Mario Konschake
title	On the robustness of in- and out-components in a temporal network.
title_short	On the robustness of in- and out-components in a temporal network.
title_full	On the robustness of in- and out-components in a temporal network.
title_fullStr	On the robustness of in- and out-components in a temporal network.
title_full_unstemmed	On the robustness of in- and out-components in a temporal network.
title_sort	on the robustness of in- and out-components in a temporal network.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/d754067735c74a8bb57823e7cecf54f9
work_keys_str_mv	AT mariokonschake ontherobustnessofinandoutcomponentsinatemporalnetwork AT hartmuthklentz ontherobustnessofinandoutcomponentsinatemporalnetwork AT franzjconraths ontherobustnessofinandoutcomponentsinatemporalnetwork AT philipphovel ontherobustnessofinandoutcomponentsinatemporalnetwork AT thomasselhorst ontherobustnessofinandoutcomponentsinatemporalnetwork
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On the robustness of in- and out-components in a temporal network.

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