A Statistical Analysis of Diffusion Dynamics in Networks

Abstract

Network diffusion is a well-known model for studying how a quantity in a network propagates to reach a steady state, where its value becomes uniformly distributed across all nodes. Previous studies of network diffusion have mainly focused on the second-smallest eigenvalue of the network Laplacian to determine the timescale associated with the slowest decaying mode. However, this study investigates the eigenspectrum of the network Laplacian in terms of its statistical properties. This method is useful for studying the statistical properties of network diffusion without calculating all eigenvalues, which can be useful for large networks. Additionally, this study introduces a condition under which the system is less diffusive and investigates how changes in the network structure influence the overall diffusion dynamics.