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Link Prediction in Bipartite Nested Networks

Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of...

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Detalles Bibliográficos
Autores principales: Medo, Matúš, Mariani, Manuel Sebastian, Lü, Linyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512339/
https://www.ncbi.nlm.nih.gov/pubmed/33265865
http://dx.doi.org/10.3390/e20100777
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author Medo, Matúš
Mariani, Manuel Sebastian
Lü, Linyuan
author_facet Medo, Matúš
Mariani, Manuel Sebastian
Lü, Linyuan
author_sort Medo, Matúš
collection PubMed
description Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.
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spelling pubmed-75123392020-11-09 Link Prediction in Bipartite Nested Networks Medo, Matúš Mariani, Manuel Sebastian Lü, Linyuan Entropy (Basel) Article Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis. MDPI 2018-10-10 /pmc/articles/PMC7512339/ /pubmed/33265865 http://dx.doi.org/10.3390/e20100777 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Medo, Matúš
Mariani, Manuel Sebastian
Lü, Linyuan
Link Prediction in Bipartite Nested Networks
title Link Prediction in Bipartite Nested Networks
title_full Link Prediction in Bipartite Nested Networks
title_fullStr Link Prediction in Bipartite Nested Networks
title_full_unstemmed Link Prediction in Bipartite Nested Networks
title_short Link Prediction in Bipartite Nested Networks
title_sort link prediction in bipartite nested networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512339/
https://www.ncbi.nlm.nih.gov/pubmed/33265865
http://dx.doi.org/10.3390/e20100777
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