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dc.contributor.authorHristova, Desislavaen
dc.contributor.authorNoulas, Anastasiosen
dc.contributor.authorBrown, Chloeen
dc.contributor.authorMusolesi, Mircoen
dc.contributor.authorMascolo, Ceciliaen
dc.date.accessioned2016-08-26T14:37:34Z
dc.date.available2016-08-26T14:37:34Z
dc.date.issued2016-07-26en
dc.identifier.issn2193-1127
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/257431
dc.description.abstractOnline social systems are multiplex in nature as multiple links may exist between the same two users across different social media. In this work, we study the geo-social properties of multiplex links, spanning more than one social network and apply their structural and interaction features to the problem of link prediction across social networking services. Exploring the intersection of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite $\textit{multilayer online social network}$, where each platform represents a layer in the network. We find that pairs of users connected on both services, have greater neighbourhood similarity and are more similar in terms of their social and spatial properties on both platforms in comparison with pairs who are connected on just one of the social networks. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that we can successfully predict links across social networking services. In addition, we also show how combining information from multiple heterogeneous networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction systems with valuable applications to social bootstrapping and friend recommendations.
dc.description.sponsorshipThis work was supported by the Project LASAGNE, Contract No. 318132 (STREP), funded by the European Commission and EPSRC through Grant GALE (EP/K019392).
dc.languageEnglishen
dc.language.isoenen
dc.publisherSpringer
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectonline social networksen
dc.subjectmedia multiplexityen
dc.subjectmultilayer networksen
dc.subjectlink predictionen
dc.titleA multilayer approach to multiplexity and link prediction in online geo-social networksen
dc.typeArticle
dc.description.versionThis is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1140/epjds/s13688-016-0087-zen
prism.number24en
prism.publicationDate2016en
prism.publicationNameEPJ Data Scienceen
prism.volume5en
dc.identifier.doi10.17863/CAM.1665
dcterms.dateAccepted2016-07-13en
rioxxterms.versionofrecord10.1140/epjds/s13688-016-0087-zen
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2016-07-26en
dc.contributor.orcidBrown, Chloe [0000-0002-9229-3351]
dc.contributor.orcidMascolo, Cecilia [0000-0001-9614-4380]
dc.identifier.eissn2193-1127
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEC FP7 CP (318132)
pubs.funder-project-idEPSRC (EP/K019392/1)
cam.orpheus.successThu Jan 30 12:57:15 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International