Stylized facts in social networks: Community-based static modeling

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Bibtex - Lataa

@article{5d30291b97e244ed9745070f66dec4c3,
title = "Stylized facts in social networks: Community-based static modeling",
abstract = "The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.",
author = "Jo, {Hang Hyun} and Yohsuke Murase and J{\'a}nos T{\"o}r{\"o}k and J{\'a}nos Kert{\'e}sz and Kimmo Kaski",
note = "| openaire: EC/H2020/662725/EU//IBSEN",
year = "2018",
month = "6",
day = "15",
doi = "10.1016/j.physa.2018.02.023",
language = "English",
volume = "500",
pages = "23--39",
journal = "Physica A: Statistical Mechanics and its Applications",
issn = "0378-4371",
publisher = "Elsevier Science B.V.",

}

RIS - Lataa

TY - JOUR

T1 - Stylized facts in social networks

T2 - Community-based static modeling

AU - Jo, Hang Hyun

AU - Murase, Yohsuke

AU - Török, János

AU - Kertész, János

AU - Kaski, Kimmo

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

PY - 2018/6/15

Y1 - 2018/6/15

N2 - The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

AB - The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

UR - http://www.scopus.com/inward/record.url?scp=85042356462&partnerID=8YFLogxK

U2 - 10.1016/j.physa.2018.02.023

DO - 10.1016/j.physa.2018.02.023

M3 - Article

VL - 500

SP - 23

EP - 39

JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

SN - 0378-4371

ER -

ID: 18143917