論文

2018年6月

Stylized facts in social networks: Community-based static modeling

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
  • Hang-Hyun Jo
  • ,
  • Yohsuke Murase
  • ,
  • Janos Torok
  • ,
  • Janos Kertesz
  • ,
  • Kimmo Kaski

500
開始ページ
23
終了ページ
39
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.physa.2018.02.023
出版者・発行元
ELSEVIER

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. (C) 2018 Elsevier B.V. All rights reserved.


リンク情報
DOI
https://doi.org/10.1016/j.physa.2018.02.023
arXiv
http://arxiv.org/abs/arXiv:1611.03664
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000430027400003&DestApp=WOS_CPL
URL
http://arxiv.org/abs/1611.03664v4
URL
http://arxiv.org/pdf/1611.03664v4 本文へのリンクあり
ID情報
  • DOI : 10.1016/j.physa.2018.02.023
  • ISSN : 0378-4371
  • eISSN : 1873-2119
  • arXiv ID : arXiv:1611.03664
  • Web of Science ID : WOS:000430027400003

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