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A split-and-transfer flow based entropic centrality

The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at...

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Autores principales: Oggier, Frédérique, Phetsouvanh, Silivanxay, Datta, Anwitaman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924417/
https://www.ncbi.nlm.nih.gov/pubmed/33816873
http://dx.doi.org/10.7717/peerj-cs.220
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author Oggier, Frédérique
Phetsouvanh, Silivanxay
Datta, Anwitaman
author_facet Oggier, Frédérique
Phetsouvanh, Silivanxay
Datta, Anwitaman
author_sort Oggier, Frédérique
collection PubMed
description The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at every node, the flow is transferred from one edge to another. The contribution of this paper is to propose a split-and-transfer flow model for entropic centrality, where at every node, the flow can actually be arbitrarily split across choices of neighbours. We show how to map this to an equivalent transfer entropic centrality set-up for the ease of computation, and carry out three case studies (an airport network, a cross-shareholding network and a Bitcoin transactions subnetwork) to illustrate the interpretation and insights linked to this new notion of centrality.
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spelling pubmed-79244172021-04-02 A split-and-transfer flow based entropic centrality Oggier, Frédérique Phetsouvanh, Silivanxay Datta, Anwitaman PeerJ Comput Sci Data Mining and Machine Learning The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at every node, the flow is transferred from one edge to another. The contribution of this paper is to propose a split-and-transfer flow model for entropic centrality, where at every node, the flow can actually be arbitrarily split across choices of neighbours. We show how to map this to an equivalent transfer entropic centrality set-up for the ease of computation, and carry out three case studies (an airport network, a cross-shareholding network and a Bitcoin transactions subnetwork) to illustrate the interpretation and insights linked to this new notion of centrality. PeerJ Inc. 2019-09-16 /pmc/articles/PMC7924417/ /pubmed/33816873 http://dx.doi.org/10.7717/peerj-cs.220 Text en ©2019 Oggier et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Oggier, Frédérique
Phetsouvanh, Silivanxay
Datta, Anwitaman
A split-and-transfer flow based entropic centrality
title A split-and-transfer flow based entropic centrality
title_full A split-and-transfer flow based entropic centrality
title_fullStr A split-and-transfer flow based entropic centrality
title_full_unstemmed A split-and-transfer flow based entropic centrality
title_short A split-and-transfer flow based entropic centrality
title_sort split-and-transfer flow based entropic centrality
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924417/
https://www.ncbi.nlm.nih.gov/pubmed/33816873
http://dx.doi.org/10.7717/peerj-cs.220
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