Abstract

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Original languageEnglish (US)
Article numbere111013
JournalPLoS One
Volume9
Issue number11
DOIs
StatePublished - Nov 7 2014

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social networks
Social Support
online courses
probabilistic models
Earthquakes
Statistical Models
earthquakes
China
Japan
Growth

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Wang, L. Z., Huang, Z. G., Rong, Z. H., Wang, X. F., & Lai, Y-C. (2014). Emergence, evolution and scaling of online social networks. PLoS One, 9(11), [e111013]. https://doi.org/10.1371/journal.pone.0111013

Emergence, evolution and scaling of online social networks. / Wang, Le Zhi; Huang, Zi Gang; Rong, Zhi Hai; Wang, Xiao Fan; Lai, Ying-Cheng.

In: PLoS One, Vol. 9, No. 11, e111013, 07.11.2014.

Research output: Contribution to journalArticle

Wang, Le Zhi ; Huang, Zi Gang ; Rong, Zhi Hai ; Wang, Xiao Fan ; Lai, Ying-Cheng. / Emergence, evolution and scaling of online social networks. In: PLoS One. 2014 ; Vol. 9, No. 11.
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