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What Big Data tells

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Török, János
dc.contributor.author Murase, Yohsuke
dc.contributor.author Jo, Hang Hyun
dc.contributor.author Kertész, János
dc.contributor.author Kaski, Kimmo
dc.date.accessioned 2018-06-18T09:19:52Z
dc.date.available 2018-06-18T09:19:52Z
dc.date.issued 2016-11-29
dc.identifier.citation Török , J , Murase , Y , Jo , H H , Kertész , J & Kaski , K 2016 , ' What Big Data tells : Sampling the social network by communication channels ' , Physical Review E , vol. 94 , no. 5 , 052319 , pp. 1-11 . https://doi.org/10.1103/PhysRevE.94.052319 en
dc.identifier.issn 1539-3755
dc.identifier.issn 1550-2376
dc.identifier.other PURE UUID: 7a9fbcd6-a14a-409f-a8f8-603e7f6d9e73
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/7a9fbcd6-a14a-409f-a8f8-603e7f6d9e73
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=84999751304&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/21523724/PhysRevE.94.052319_1.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/31918
dc.description | openaire: EC/H2020/662725/EU//IBSEN
dc.description.abstract Big Data has become the primary source of understanding the structure and dynamics of the society at large scale. The network of social interactions can be considered as a multiplex, where each layer corresponds to one communication channel and the aggregate of all of them constitutes the entire social network. However, usually one has information only about one of the channels or even a part of it, which should be considered as a subset or sample of the whole. Here we introduce a model based on a natural bilateral communication channel selection mechanism, which for one channel leads to consistent changes in the network properties. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get a monotonically decreasing distribution as observed in empirical studies of single-channel data. We also find that assortativity may occur or get strengthened due to the sampling method. We analyze the far-reaching consequences of our findings. en
dc.format.extent 1-11
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/662725/EU//IBSEN
dc.relation.ispartofseries PHYSICAL REVIEW E en
dc.relation.ispartofseries Volume 94, issue 5 en
dc.rights openAccess en
dc.title What Big Data tells en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Budapest University of Technology and Economics
dc.contributor.department RIKEN
dc.contributor.department Kaski Kimmo group
dc.contributor.department Department of Computer Science
dc.identifier.urn URN:NBN:fi:aalto-201806183336
dc.identifier.doi 10.1103/PhysRevE.94.052319
dc.type.version publishedVersion

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