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Link prediction on bipartite networks using matrix factorization with negative sample selection
We propose a new method for bipartite link prediction using matrix factorization with negative sample selection. Bipartite link prediction is a problem that aims to predict the missing links or relations in a bipartite network. One of the most popular solutions to the problem is via matrix factoriza...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431684/ https://www.ncbi.nlm.nih.gov/pubmed/37585433 http://dx.doi.org/10.1371/journal.pone.0289568 |
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author | Peng, Siqi Yamamoto, Akihiro Ito, Kimihito |
author_facet | Peng, Siqi Yamamoto, Akihiro Ito, Kimihito |
author_sort | Peng, Siqi |
collection | PubMed |
description | We propose a new method for bipartite link prediction using matrix factorization with negative sample selection. Bipartite link prediction is a problem that aims to predict the missing links or relations in a bipartite network. One of the most popular solutions to the problem is via matrix factorization (MF), which performs well but requires reliable information on both absent and present network links as training samples. This, however, is sometimes unavailable since there is no ground truth for absent links. To solve the problem, we propose a technique called negative sample selection, which selects reliable negative training samples using formal concept analysis (FCA) of a given bipartite network in advance of the preceding MF process. We conduct experiments on two hypothetical application scenarios to prove that our joint method outperforms the raw MF-based link prediction method as well as all other previously-proposed unsupervised link prediction methods. |
format | Online Article Text |
id | pubmed-10431684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104316842023-08-17 Link prediction on bipartite networks using matrix factorization with negative sample selection Peng, Siqi Yamamoto, Akihiro Ito, Kimihito PLoS One Research Article We propose a new method for bipartite link prediction using matrix factorization with negative sample selection. Bipartite link prediction is a problem that aims to predict the missing links or relations in a bipartite network. One of the most popular solutions to the problem is via matrix factorization (MF), which performs well but requires reliable information on both absent and present network links as training samples. This, however, is sometimes unavailable since there is no ground truth for absent links. To solve the problem, we propose a technique called negative sample selection, which selects reliable negative training samples using formal concept analysis (FCA) of a given bipartite network in advance of the preceding MF process. We conduct experiments on two hypothetical application scenarios to prove that our joint method outperforms the raw MF-based link prediction method as well as all other previously-proposed unsupervised link prediction methods. Public Library of Science 2023-08-16 /pmc/articles/PMC10431684/ /pubmed/37585433 http://dx.doi.org/10.1371/journal.pone.0289568 Text en © 2023 Peng 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, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Peng, Siqi Yamamoto, Akihiro Ito, Kimihito Link prediction on bipartite networks using matrix factorization with negative sample selection |
title | Link prediction on bipartite networks using matrix factorization with negative sample selection |
title_full | Link prediction on bipartite networks using matrix factorization with negative sample selection |
title_fullStr | Link prediction on bipartite networks using matrix factorization with negative sample selection |
title_full_unstemmed | Link prediction on bipartite networks using matrix factorization with negative sample selection |
title_short | Link prediction on bipartite networks using matrix factorization with negative sample selection |
title_sort | link prediction on bipartite networks using matrix factorization with negative sample selection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431684/ https://www.ncbi.nlm.nih.gov/pubmed/37585433 http://dx.doi.org/10.1371/journal.pone.0289568 |
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