Network embedding from the line graph: Random walkers and boosted classification
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Título: | Network embedding from the line graph: Random walkers and boosted classification |
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Autor/es: | Lozano, Miguel Angel | Escolano, Francisco | Curado, Manuel | Hancock, Edwin R. |
Grupo/s de investigación o GITE: | Laboratorio de Investigación en Visión Móvil (MVRLab) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Network embedding | SGNS | Line graph | Spectral theory |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | mar-2021 |
Editor: | Elsevier |
Cita bibliográfica: | Pattern Recognition Letters. 2021, 143: 36-42. https://doi.org/10.1016/j.patrec.2020.12.018 |
Resumen: | In this paper, we propose to embed edges instead of nodes using state-of-the-art neural/factorization methods (DeepWalk, node2vec, NetMF). These methods produce latent representations based on co-ocurrence statistics by simulating fixed-length random walks and then taking bags-of-vectors as the input to the Skip Gram Learning with Negative Sampling (SGNS). We commence by expressing commute times embedding as matrix factorization, and thus relating this embedding to those of DeepWalk and node2vec. Recent results showing formal links between all these methods via the spectrum of graph Laplacian, are then extended to understand the results obtained by SGNS when we embed edges instead of nodes. Since embedding edges is equivalent to embedding nodes in the line graph, we proceed to combine both existing formal characterizations of the line graphs and empirical evidence in order to explain why this embedding dramatically outperforms its nodal counterpart in multi-label classification tasks. |
Patrocinador/es: | M.A. Lozano, M. Curado and F. Escolano are funded by the project RTI2018-096223-B-I00 of the Spanish Government. |
URI: | http://hdl.handle.net/10045/112629 |
ISSN: | 0167-8655 (Print) | 1872-7344 (Online) |
DOI: | 10.1016/j.patrec.2020.12.018 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2021 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.patrec.2020.12.018 |
Aparece en las colecciones: | INV - MVRLab - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Lozano_etal_2021_PatternRecognLett_final.pdf | 1,55 MB | Adobe PDF | Abrir Vista previa | |
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