Correlated Dynamics in Egocentric Communication Networks

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Karsai, Márton Kaski, Kimmo Kertész, János 2017-05-11T08:38:00Z 2017-05-11T08:38:00Z 2012
dc.identifier.citation Karsai , M , Kaski , K & Kertész , J 2012 , ' Correlated Dynamics in Egocentric Communication Networks ' PLOS ONE , vol 7 , no. 7 , e40612 , pp. 1-9 . DOI: 10.1371/journal.pone.0040612 en
dc.identifier.issn 1932-6203
dc.identifier.other PURE UUID: 92cf9bf8-1499-4db5-9eee-949d9eafac94
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE FILEURL:
dc.description.abstract We investigate the communication sequences of millions of people through two different channels and analyse the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbours, thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constraints (for short messages) and partly to the human behavioural features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics. en
dc.format.extent 1-9
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries PLOS ONE en
dc.relation.ispartofseries Volume 7, issue 7 en
dc.rights openAccess en
dc.title Correlated Dynamics in Egocentric Communication Networks en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science en
dc.identifier.urn URN:NBN:fi:aalto-201705114081
dc.identifier.doi 10.1371/journal.pone.0040612
dc.type.version publishedVersion

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