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

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Details

Original languageEnglish
Article number052319
Pages (from-to)1-11
JournalPhysical Review E
Volume94
Issue number5
Publication statusPublished - 29 Nov 2016
MoE publication typeA1 Journal article-refereed

Researchers

Research units

  • Budapest University of Technology and Economics
  • Central European University
  • RIKEN
  • Pohang University of Science and Technology

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.

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