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Estimating tie strength in social networks using temporal communication data

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Urena Carrion, Javier
dc.contributor.author Saramäki, Jari
dc.contributor.author Kivelä, Mikko
dc.date.accessioned 2020-12-31T08:48:54Z
dc.date.available 2020-12-31T08:48:54Z
dc.date.issued 2020-12-03
dc.identifier.citation Urena Carrion , J , Saramäki , J & Kivelä , M 2020 , ' Estimating tie strength in social networks using temporal communication data ' , EPJ Data Science , vol. 9 , no. 1 , 37 . https://doi.org/10.1140/epjds/s13688-020-00256-5 en
dc.identifier.issn 2193-1127
dc.identifier.other PURE UUID: d319eadd-3e6d-4dcd-b502-4a4821f54028
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/d319eadd-3e6d-4dcd-b502-4a4821f54028
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85097581009&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/54098792/Ure_a_Carrion2020_Article_EstimatingTieStrengthInSocialN.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/101635
dc.description.abstract Even though the concept of tie strength is central in social network analysis, it is difficult to quantify how strong social ties are. One typical way of estimating tie strength in data-driven studies has been to simply count the total number or duration of contacts between two people. This, however, disregards many features that can be extracted from the rich data sets used for social network reconstruction. Here, we focus on contact data with temporal information. We systematically study how features of the contact time series are related to topological features usually associated with tie strength. We focus on a large mobile-phone dataset and measure a number of properties of the contact time series for each tie and use these to predict the so-called neighbourhood overlap, a feature related to strong ties in the sociological literature. We observe a strong relationship between temporal features and the neighbourhood overlap, with many features outperforming simple contact counts. Features that stand out include the number of days with calls, number of bursty cascades, typical times of contacts, and temporal stability. These are also seen to correlate with the overlap in diverse smaller communication datasets studied for reference. Taken together, our results suggest that such temporal features could be useful for inferring social network structure from communication data. en
dc.format.extent 20
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Springer Science + Business Media
dc.relation.ispartofseries EPJ Data Science en
dc.relation.ispartofseries Volume 9, issue 1 en
dc.rights openAccess en
dc.title Estimating tie strength in social networks using temporal communication data en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Saramäki J.
dc.contributor.department Helsinki Institute for Information Technology (HIIT)
dc.contributor.department Professorship Kivelä Mikko
dc.contributor.department Department of Computer Science en
dc.subject.keyword social network
dc.subject.keyword Tie strength
dc.subject.keyword call detail record
dc.subject.keyword communication networks
dc.identifier.urn URN:NBN:fi:aalto-2020123160456
dc.identifier.doi 10.1140/epjds/s13688-020-00256-5
dc.type.version publishedVersion

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