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dc.contributor.authorZhao, K
dc.contributor.authorKarsai, M
dc.contributor.authorBianconi, G
dc.descriptionPMCID: PMC3241622
dc.descriptionThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.description.abstractHuman dynamical social networks encode information and are highly adaptive. To characterize the information encoded in the fast dynamics of social interactions, here we introduce the entropy of dynamical social networks. By analysing a large dataset of phone-call interactions we show evidence that the dynamical social network has an entropy that depends on the time of the day in a typical week-day. Moreover we show evidence for adaptability of human social behavior showing data on duration of phone-call interactions that significantly deviates from the statistics of duration of face-to-face interactions. This adaptability of behavior corresponds to a different information content of the dynamics of social human interactions. We quantify this information by the use of the entropy of dynamical networks on realistic models of social interactions.
dc.format.extente28116 - ?
dc.subjectInterpersonal Relations
dc.subjectModels, Biological
dc.subjectSocial Support
dc.subjectTime Factors
dc.titleEntropy of dynamical social networks.
dc.typeJournal Article
dc.relation.isPartOfPLoS One
dc.relation.isPartOfPLoS One
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Mathematical Sciences - Staff and Research Students

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