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Introduction

  • Haiyan Wang
  • Feng Wang
  • Kuai Xu
Chapter
  • 45 Downloads
Part of the Surveys and Tutorials in the Applied Mathematical Sciences book series (STAMS, volume 7)

Abstract

Online social networks (OSNs) such as Twitter and Facebook, emerging as the “model organism” of Big Data, have gained tremendous popularity for the platforms they provided for information exchange. Much of prior work on information diffusion over online social networks has been based on empirical and statistical approaches. The majority of dynamical models arising from information diffusion over online social networks are ordinary differential equations (ODEs). Recently, the authors proposed to use partial differential equations (PDEs) to model information diffusion in online social networks and introduced a new transdisciplinary architecture for modeling information diffusion. These studies demonstrate fascinating connections between advanced mathematics and online social networks.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Haiyan Wang
    • 1
  • Feng Wang
    • 1
  • Kuai Xu
    • 1
  1. 1.School of Mathematical & Natural SciencesArizona State UniversityPhoenixUSA

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