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

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.

Description

| openaire: EC/H2020/662725/EU//IBSEN

DOI

10.1103/PhysRevE.94.052319

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

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201806183336

Collections

[article-cris] Perustieteiden korkeakoulu / SCI

Show all metadata

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

Access rights

URL

Journal Title

Journal ISSN

Volume Title

Authors

Date

Department

Major/Subject

Mcode

Degree programme

Language

Pages

Series

Abstract

Description

Keywords

Other note

DOI

Citation

Permanent link to this item

Collections