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A Nonlinear Dynamical Approach to the Interpretation of Microblogging Network Complexity

  • 2018
  • OriginalPaper
  • Chapter
Published in:
Authors
Andrey Dmitriev
Victor Dmitriev
Olga Tsukanova
Svetlana Maltseva
Published in
Complex Networks & Their Applications VI
Publisher
Springer International Publishing

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Abstract

The present paper is devoted to the research into complexity of microblogging social networks regardless of their internal structure. This approach assumes using the results of nonlinear dynamical analysis of signals generated by the networks. The existence of the main indicators of social network complexity, such as scale invariance, tendency to unexpected and/or extraordinary events, non-equilibrium state and emergent properties, are shown using the example of Twitter. As a result, it is determined that the probability density function for a Twitter time series is a q-exponential (Tsallis) distribution and that the Kaulakys equation is the most adequate nonlinear random dynamical system for modeling of signals in social networks.

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previous chapter Ranking in Dynamic Graphs Using Exponential Centrality
next chapter Capturing the Dynamics of Hashtag-Communities
Title
A Nonlinear Dynamical Approach to the Interpretation of Microblogging Network Complexity
Authors
Andrey Dmitriev
Victor Dmitriev
Olga Tsukanova
Svetlana Maltseva
Copyright Year
2018
Book
Complex Networks & Their Applications VI

Print ISBN: 978-3-319-72149-1

Electronic ISBN: 978-3-319-72150-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-72150-7_32
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