論文

2016年11月

What Big Data tells: Sampling the social network by communication channels

PHYSICAL REVIEW E
  • Janos Toeroek
  • ,
  • Yohsuke Murase
  • ,
  • Hang-Hyun Jo
  • ,
  • Janos Kertesz
  • ,
  • Kimmo Kaski

94
5
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1103/PhysRevE.94.052319
出版者・発行元
AMER PHYSICAL SOC

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.


リンク情報
DOI
https://doi.org/10.1103/PhysRevE.94.052319
arXiv
http://arxiv.org/abs/arXiv:1511.08749
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000388834800006&DestApp=WOS_CPL
URL
http://arxiv.org/abs/1511.08749v4
URL
http://arxiv.org/pdf/1511.08749v4 本文へのリンクあり
ID情報
  • DOI : 10.1103/PhysRevE.94.052319
  • ISSN : 2470-0045
  • eISSN : 2470-0053
  • arXiv ID : arXiv:1511.08749
  • Web of Science ID : WOS:000388834800006

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