An information theoretic approach to link prediction in multiplex networks

Abstract The entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range o...

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Autores principales:	Seyed Hossein Jafari, Amir Mahdi Abdolhosseini-Qomi, Masoud Asadpour, Maseud Rahgozar, Naser Yazdani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f016a262ba894e5ca47c0f2f7b0d48ad
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spelling	oai:doaj.org-article:f016a262ba894e5ca47c0f2f7b0d48ad2021-12-02T16:05:54ZAn information theoretic approach to link prediction in multiplex networks10.1038/s41598-021-92427-12045-2322https://doaj.org/article/f016a262ba894e5ca47c0f2f7b0d48ad2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92427-1https://doaj.org/toc/2045-2322Abstract The entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.Seyed Hossein JafariAmir Mahdi Abdolhosseini-QomiMasoud AsadpourMaseud RahgozarNaser YazdaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-21 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Seyed Hossein Jafari Amir Mahdi Abdolhosseini-Qomi Masoud Asadpour Maseud Rahgozar Naser Yazdani An information theoretic approach to link prediction in multiplex networks
description	Abstract The entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.
format	article
author	Seyed Hossein Jafari Amir Mahdi Abdolhosseini-Qomi Masoud Asadpour Maseud Rahgozar Naser Yazdani
author_facet	Seyed Hossein Jafari Amir Mahdi Abdolhosseini-Qomi Masoud Asadpour Maseud Rahgozar Naser Yazdani
author_sort	Seyed Hossein Jafari
title	An information theoretic approach to link prediction in multiplex networks
title_short	An information theoretic approach to link prediction in multiplex networks
title_full	An information theoretic approach to link prediction in multiplex networks
title_fullStr	An information theoretic approach to link prediction in multiplex networks
title_full_unstemmed	An information theoretic approach to link prediction in multiplex networks
title_sort	information theoretic approach to link prediction in multiplex networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f016a262ba894e5ca47c0f2f7b0d48ad
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An information theoretic approach to link prediction in multiplex networks

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