Burstiness and fractional diffusion on complex networks

  • Sarah de NigrisEmail author
  • Anthony Hastir
  • Renaud Lambiotte
Regular Article


Many dynamical processes on real world networks display complex temporal patterns as, for instance, a fat-tailed distribution of inter-events times, leading to heterogeneous waiting times between events. In this work, we focus on distributions whose average inter-event time diverges, and study its impact on the dynamics of random walkers on networks. The process can naturally be described, in the long time limit, in terms of Riemann-Liouville fractional derivatives. We show that all the dynamical modes possess, in the asymptotic regime, the same power law relaxation, which implies that the dynamics does not exhibit time-scale separation between modes, and that no mode can be neglected versus another one, even for long times. Our results are then confirmed by numerical simulations.


Statistical and Nonlinear Physics 


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Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Sarah de Nigris
    • 1
    Email author
  • Anthony Hastir
    • 1
  • Renaud Lambiotte
    • 1
  1. 1.naXys, University of NamurNamurBelgium

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