Reducibility of higher-order networks from dynamics

TL;DR

This paper introduces an information-theoretic method to evaluate when higher-order network models are necessary over simpler pairwise models, based on their impact on diffusion dynamics and structural properties.

Contribution

It develops a quantitative framework to assess the reducibility of higher-order networks to pairwise interactions, considering structure and functional information.

Findings

Some systems retain essential higher-order interactions

In certain biological and technological networks, higher-order structure reduces to pairwise interactions

Nestedness and degree heterogeneity influence reducibility

Abstract

Empirical complex systems can be characterized not only by pairwise interactions, but also by higher-order (group) interactions influencing collective phenomena, from metabolic reactions to epidemics. Nevertheless, higher-order networks' apparent superior descriptive power -- compared to classical pairwise networks -- comes with a much increased model complexity and computational cost, challenging their application. Consequently, it is of paramount importance to establish a quantitative method to determine when such a modeling framework is advantageous with respect to pairwise models, and to which extent it provides a valuable description of empirical systems. Here, we propose an information-theoretic framework, accounting for how structure affect diffusion behaviors, quantifying the entropic cost and distinguishability of higher-order interactions to assess their reducibility to lower-order structures while preserving relevant functional information. Empirical analyses indicate that some systems retain essential higher-order structure, whereas in some technological and biological networks it collapses to pairwise interactions. With controlled randomization procedures, we investigate the role of nestedness and degree heterogeneity in this reducibility process. Our findings contribute to ongoing efforts to minimize the dimensionality of models for complex systems.