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dc.contributor.authorBotta, F
dc.contributor.authorDel Genio, CI
dc.date.accessioned2020-07-23T09:06:16Z
dc.date.issued2017-03-23
dc.description.abstractBeing able to characterise the patterns of communications between individuals across different time scales is of great importance in understanding people's social interactions. Here, we present a detailed analysis of the community structure of the network of mobile phone calls in the metropolitan area of Milan revealing temporal patterns of communications between people. We show that circadian and weekly patterns can be found in the evolution of communities, presenting evidence that these cycles arise not only at the individual level but also at that of social groups. Our findings suggest that these trends are present across a range of time scales, from hours to days and weeks, and can be used to detect socially relevant events.en_GB
dc.description.sponsorshipEPSRCen_GB
dc.description.sponsorshipEuropean Commissionen_GB
dc.identifier.citationVol. 12 (3): e0174198.en_GB
dc.identifier.doi10.1371/journal.pone.0174198
dc.identifier.grantnumberEP/E501311/1en_GB
dc.identifier.grantnumber288021en_GB
dc.identifier.grantnumberEP/K000128/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/122094
dc.language.isoenen_GB
dc.publisherPublic Library of Scienceen_GB
dc.rightsCopyright: © 2017 Botta, Genio. This 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.en_GB
dc.subjectcommunity structureen_GB
dc.subjectcell phonesen_GB
dc.subjectTelecommunicationsen_GB
dc.subjectChronobiologyen_GB
dc.subjectgeographyen_GB
dc.subjectMathematical modelsen_GB
dc.subjectNetwork analysisen_GB
dc.subjecturban areasen_GB
dc.titleAnalysis of the communities of an urban mobile phone networken_GB
dc.typeArticleen_GB
dc.date.available2020-07-23T09:06:16Z
dc.descriptionThis is the final version. Available from Public Library of Science via the DOI in this record. en_GB
dc.descriptionData Availability: Data available at: Telecom Italia Big Data Challenge 2014, https://dandelion.eu/datamine/open-big-data/.en_GB
dc.identifier.eissn1932-6203
dc.identifier.journalPLoS ONEen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2017-03-06
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2017-03-06
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2020-07-23T09:00:11Z
refterms.versionFCDVoR
refterms.dateFOA2020-07-23T09:06:20Z
refterms.panelBen_GB


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Copyright:  © 2017 Botta, Genio. This 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.
Except where otherwise noted, this item's licence is described as Copyright: © 2017 Botta, Genio. This 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.