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Belief Functions for the Importance Assessment in Multiplex Networks
We apply Dempster-Shafer theory in order to reveal important elements in undirected weighted networks. We estimate cooperation of each node with different groups of vertices that surround it via construction of belief functions. The obtained intensities of cooperation are further redistributed over...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274755/ http://dx.doi.org/10.1007/978-3-030-50143-3_22 |
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author | Lepskiy, Alexander Meshcheryakova, Natalia |
author_facet | Lepskiy, Alexander Meshcheryakova, Natalia |
author_sort | Lepskiy, Alexander |
collection | PubMed |
description | We apply Dempster-Shafer theory in order to reveal important elements in undirected weighted networks. We estimate cooperation of each node with different groups of vertices that surround it via construction of belief functions. The obtained intensities of cooperation are further redistributed over all elements of a particular group of nodes that results in pignistic probabilities of node-to-node interactions. Finally, pairwise interactions can be aggregated into the centrality vector that ranks nodes with respect to derived values. We also adapt the proposed model to multiplex networks. In this type of networks nodes can be differently connected with each other on several levels of interaction. Various combination rules help to analyze such systems as a single entity, that has many advantages in the study of complex systems. In particular, Dempster rule takes into account the inconsistency in initial data that has an impact on the final centrality ranking. We also provide a numerical example that illustrates the distinctive features of the proposed model. Additionally, we establish analytical relations between a proposed measure and classical centrality measures for particular graph configurations. |
format | Online Article Text |
id | pubmed-7274755 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72747552020-06-08 Belief Functions for the Importance Assessment in Multiplex Networks Lepskiy, Alexander Meshcheryakova, Natalia Information Processing and Management of Uncertainty in Knowledge-Based Systems Article We apply Dempster-Shafer theory in order to reveal important elements in undirected weighted networks. We estimate cooperation of each node with different groups of vertices that surround it via construction of belief functions. The obtained intensities of cooperation are further redistributed over all elements of a particular group of nodes that results in pignistic probabilities of node-to-node interactions. Finally, pairwise interactions can be aggregated into the centrality vector that ranks nodes with respect to derived values. We also adapt the proposed model to multiplex networks. In this type of networks nodes can be differently connected with each other on several levels of interaction. Various combination rules help to analyze such systems as a single entity, that has many advantages in the study of complex systems. In particular, Dempster rule takes into account the inconsistency in initial data that has an impact on the final centrality ranking. We also provide a numerical example that illustrates the distinctive features of the proposed model. Additionally, we establish analytical relations between a proposed measure and classical centrality measures for particular graph configurations. 2020-05-15 /pmc/articles/PMC7274755/ http://dx.doi.org/10.1007/978-3-030-50143-3_22 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Lepskiy, Alexander Meshcheryakova, Natalia Belief Functions for the Importance Assessment in Multiplex Networks |
title | Belief Functions for the Importance Assessment in Multiplex Networks |
title_full | Belief Functions for the Importance Assessment in Multiplex Networks |
title_fullStr | Belief Functions for the Importance Assessment in Multiplex Networks |
title_full_unstemmed | Belief Functions for the Importance Assessment in Multiplex Networks |
title_short | Belief Functions for the Importance Assessment in Multiplex Networks |
title_sort | belief functions for the importance assessment in multiplex networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274755/ http://dx.doi.org/10.1007/978-3-030-50143-3_22 |
work_keys_str_mv | AT lepskiyalexander belieffunctionsfortheimportanceassessmentinmultiplexnetworks AT meshcheryakovanatalia belieffunctionsfortheimportanceassessmentinmultiplexnetworks |