MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Abstract Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been spec...

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Autores principales:	Léo Pio-Lopez, Alberto Valdeolivas, Laurent Tichit, Élisabeth Remy, Anaïs Baudot
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8ad03b7ac4f64e018d34f0f53b0b45b0
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spelling	oai:doaj.org-article:8ad03b7ac4f64e018d34f0f53b0b45b02021-12-02T17:32:59ZMultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach10.1038/s41598-021-87987-12045-2322https://doaj.org/article/8ad03b7ac4f64e018d34f0f53b0b45b02021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87987-1https://doaj.org/toc/2045-2322Abstract Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE .Léo Pio-LopezAlberto ValdeolivasLaurent TichitÉlisabeth RemyAnaïs BaudotNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Léo Pio-Lopez Alberto Valdeolivas Laurent Tichit Élisabeth Remy Anaïs Baudot MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
description	Abstract Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE .
format	article
author	Léo Pio-Lopez Alberto Valdeolivas Laurent Tichit Élisabeth Remy Anaïs Baudot
author_facet	Léo Pio-Lopez Alberto Valdeolivas Laurent Tichit Élisabeth Remy Anaïs Baudot
author_sort	Léo Pio-Lopez
title	MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
title_short	MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
title_full	MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
title_fullStr	MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
title_full_unstemmed	MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
title_sort	multiverse: a multiplex and multiplex-heterogeneous network embedding approach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8ad03b7ac4f64e018d34f0f53b0b45b0
work_keys_str_mv	AT leopiolopez multiverseamultiplexandmultiplexheterogeneousnetworkembeddingapproach AT albertovaldeolivas multiverseamultiplexandmultiplexheterogeneousnetworkembeddingapproach AT laurenttichit multiverseamultiplexandmultiplexheterogeneousnetworkembeddingapproach AT elisabethremy multiverseamultiplexandmultiplexheterogeneousnetworkembeddingapproach AT anaisbaudot multiverseamultiplexandmultiplexheterogeneousnetworkembeddingapproach
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MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

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