Diffusion on hierarchical systems of weakly-coupled networks

TL;DR

This paper develops a theoretical framework for understanding diffusion dynamics on weakly-coupled hierarchical networks, revealing how particles distribute and entropy evolves across different network scales.

Contribution

It introduces an adiabatic approximation reducing complex network diffusion to a Markov chain, enabling analysis of hierarchical systems and hidden hierarchy detection.

Findings

Derived a Fick's Law-like transport equation with a driving force.

Confirmed the framework through numerical simulations.

Showed equilibrium distribution depends only on sub-network parameters.

Abstract

We analyse diffusion dynamics on weakly-coupled networks (interconnected networks) by means of separation of time scales. Using an adiabatic approximation we reduced the system dynamics to a Markov chain with aggregated variables and derived a transport equation that is analogous to Fick's First Law and includes a driving force. Entropy production is a sum of microscopic entropy transport, which results from the particle's migration between networks of different topologies and macroscopic entropy production of the Markov chain. Equilibrium particles partition between different sub-networks depends only on internal sub-network parameters. Our framework, confirmed by numerical simulations, is also useful for considering diffusion in nested systems corresponding to hierarchical networks with several different time scales thus it can serve to uncover hidden hierarchy levels from observations of diffusion processes.