The role of information diffusion in the evolution of social networks

  title={The role of information diffusion in the evolution of social networks},
  author={Lilian Weng and Jacob Ratkiewicz and N. Perra and B. Gonçalves and C. Castillo and F. Bonchi and R. Schifanella and F. Menczer and A. Flammini},
  • Lilian Weng, Jacob Ratkiewicz, +6 authors A. Flammini
  • Published 2013
  • Computer Science, Physics
  • ArXiv
  • Every day millions of users are connected through online social networks, generating a rich trove of data that allows us to study the mechanisms behind human interactions. Triadic closure has been treated as the major mechanism for creating social links: if Alice follows Bob and Bob follows Charlie, Alice will follow Charlie. Here we present an analysis of longitudinal micro-blogging data, revealing a more nuanced view of the strategies employed by users when expanding their social circles… CONTINUE READING

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    Publications referenced by this paper.
    Microscopic evolution of social networks
    • 728
    • Highly Influential
    • PDF
    The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter
    • 145
    • Highly Influential
    • PDF
    Networks of strong ties
    • 106
    • Highly Influential
    • PDF
    Statistical Data Analysis
    • W. Owen
    • Computer Science, Mathematics
    • 2000
    • 322
    • Highly Influential
    Emergence of scaling in random graphs
    • 113
    • Highly Influential
    Heider vs
    • 2007