Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783586135045308416 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7512339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT medomatus linkpredictioninbipartitenestednetworks AT marianimanuelsebastian linkpredictioninbipartitenestednetworks AT lulinyuan linkpredictioninbipartitenestednetworks |