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

Research output: Contribution to journalArticleScientificpeer-review

Standard

What Big Data tells : Sampling the social network by communication channels. / Török, János; Murase, Yohsuke; Jo, Hang Hyun; Kertész, János; Kaski, Kimmo.

In: Physical Review E, Vol. 94, No. 5, 052319, 29.11.2016, p. 1-11.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

APA

Vancouver

Author

Bibtex - Download

@article{7a9fbcd6a14a409fa8f8603e7f6d9e73,
title = "What Big Data tells: Sampling the social network by communication channels",
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.",
author = "J{\'a}nos T{\"o}r{\"o}k and Yohsuke Murase and Jo, {Hang Hyun} and J{\'a}nos Kert{\'e}sz and Kimmo Kaski",
note = "| openaire: EC/H2020/662725/EU//IBSEN",
year = "2016",
month = "11",
day = "29",
doi = "10.1103/PhysRevE.94.052319",
language = "English",
volume = "94",
pages = "1--11",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "5",

}

RIS - Download

TY - JOUR

T1 - What Big Data tells

T2 - Sampling the social network by communication channels

AU - Török, János

AU - Murase, Yohsuke

AU - Jo, Hang Hyun

AU - Kertész, János

AU - Kaski, Kimmo

N1 - | openaire: EC/H2020/662725/EU//IBSEN

PY - 2016/11/29

Y1 - 2016/11/29

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84999751304&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.94.052319

DO - 10.1103/PhysRevE.94.052319

M3 - Article

VL - 94

SP - 1

EP - 11

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 5

M1 - 052319

ER -

ID: 9790181