Network heterogeneity and node capacity lead to heterogeneous scaling of fluctuations in random walks on graphs

TL;DR

This paper investigates how network topology and node capacity influence the scaling behavior of fluctuations in random walks on graphs, revealing multiple coexisting scaling laws in heterogeneous networks.

Contribution

It provides exact results for star networks and demonstrates through simulations that heterogeneity and finite capacity cause diverse fluctuation scaling behaviors.

Findings

Network heterogeneity amplifies external noise effects.

Multiple scaling relationships coexist in heterogeneous networks.

Finite node capacity leads to heterogeneous fluctuation scaling.

Abstract

Random walks are one of the best investigated dynamical processes on graphs. A particularly fascinating phenomenon is the scaling relationship of fluctuations $\sigma $ with the average flux $\langle f \rangle $. Here we analyze how network topology and nodes with finite capacity lead to deviations from a simple scaling law $\sigma \sim \langle f \rangle ^\alpha$. Sources of randomness are the random walk itself (internal noise) and the fluctuation of the number of walkers (external noise). We obtained exact results for the extreme case of a star network which are indicative of the behavior of large scale systems with a broad degree distribution.The latter are subsequently studied using Monte Carlo simulations. We find that the network heterogeneity amplifies the effects of external noise. By computing the `effective' scaling of each node we show that multiple scaling relationships can coexist in a graph with a heterogeneous degree distribution at an intermediate level of external noise. Finally, we analyze the effect of a finite capacity of nodes for random walkers and find that this also can lead to a heterogeneous scaling of fluctuations.