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(Hyper)graph Kernels over Simplicial Complexes
Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinfor...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597323/ https://www.ncbi.nlm.nih.gov/pubmed/33286924 http://dx.doi.org/10.3390/e22101155 |
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author | Martino, Alessio Rizzi, Antonello |
author_facet | Martino, Alessio Rizzi, Antonello |
author_sort | Martino, Alessio |
collection | PubMed |
description | Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered. In this paper, four (hyper)graph kernels are proposed and their efficiency and effectiveness are compared in a twofold fashion. First, by inferring the simplicial complexes on the top of underlying graphs and by performing a comparison among 18 benchmark datasets against state-of-the-art approaches; second, by facing a real-world case study (i.e., metabolic pathways classification) where input data are natively represented by hypergraphs. With this work, we aim at fostering the extension of graph kernels towards hypergraphs and, more in general, bridging the gap between structural pattern recognition and the domain of hypergraphs. |
format | Online Article Text |
id | pubmed-7597323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75973232020-11-09 (Hyper)graph Kernels over Simplicial Complexes Martino, Alessio Rizzi, Antonello Entropy (Basel) Article Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered. In this paper, four (hyper)graph kernels are proposed and their efficiency and effectiveness are compared in a twofold fashion. First, by inferring the simplicial complexes on the top of underlying graphs and by performing a comparison among 18 benchmark datasets against state-of-the-art approaches; second, by facing a real-world case study (i.e., metabolic pathways classification) where input data are natively represented by hypergraphs. With this work, we aim at fostering the extension of graph kernels towards hypergraphs and, more in general, bridging the gap between structural pattern recognition and the domain of hypergraphs. MDPI 2020-10-14 /pmc/articles/PMC7597323/ /pubmed/33286924 http://dx.doi.org/10.3390/e22101155 Text en © 2020 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 Martino, Alessio Rizzi, Antonello (Hyper)graph Kernels over Simplicial Complexes |
title | (Hyper)graph Kernels over Simplicial Complexes |
title_full | (Hyper)graph Kernels over Simplicial Complexes |
title_fullStr | (Hyper)graph Kernels over Simplicial Complexes |
title_full_unstemmed | (Hyper)graph Kernels over Simplicial Complexes |
title_short | (Hyper)graph Kernels over Simplicial Complexes |
title_sort | (hyper)graph kernels over simplicial complexes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597323/ https://www.ncbi.nlm.nih.gov/pubmed/33286924 http://dx.doi.org/10.3390/e22101155 |
work_keys_str_mv | AT martinoalessio hypergraphkernelsoversimplicialcomplexes AT rizziantonello hypergraphkernelsoversimplicialcomplexes |