Structural prediction of super-diffusion in multiplex networks

TL;DR

This paper introduces a structural parameter based on minimum node strength to predict super-diffusion in multiplex networks and provides analytical bounds for various network structures.

Contribution

It proposes a new structural predictor for super-diffusion and develops an analytical framework for understanding its occurrence in multiplex networks.

Findings

Minimum node strength effectively predicts super-diffusion.

Analytical bounds for multiplex network structures are derived.

Inter-layer connection arrangements influence super-diffusion occurrence.

Abstract

Diffusion dynamics in multiplex networks can model a diverse number of real-world processes. In some specific configurations of these systems, the super-diffusion phenomenon arises, in which the diffusion is faster in the multiplex network than in any of its layers. Many studies attempt to characterize this phenomenon by examining its dependency on structural properties of the network, such as overlap, average degree, network dissimilarity, and others. While certain properties show a correlation with super-diffusion in specific networks, a broader characterization is still missing. Here, we introduce a structural parameter based on the minimum node strength that effectively predicts the occurrence of super-diffusion in multiplex networks. Additionally, we propose a novel framework for deriving analytical bounds for several multiplex networks structures. Finally, we analyze and justify why certain arrangements of the inter-layer connections induce super-diffusion. These findings provide novel insights into the super-diffusion phenomenon and the interplay between network structure and dynamics.