Fractal conditional correlation dimension infers complex causal networks

HIGHLIGHTS

• What: The authors focus on detecting causality in the networks, assuming the networks are not synchronized. The aim of this paper is to quantify the causal inference between the subsystems in a network in the geometric sense. Expanding upon the fractal geometric concepts of the consequence of information flow, the authors develop the optimal conditional correlation dimensional geometric information flow principle (GeoC) that resembles the oCSE principle previously proposed by Sun et_al . The authors aim to extend the previous concept of correlation dimension geometric information flow to the networks.
• Who: Özge Canlı Usta and Erik . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.