Cross-order induced behaviors in contagion dynamics on higher-order networks

TL;DR

This paper investigates how higher-order interactions in complex networks lead to emergent behaviors, revealing that certain information-theoretic measures can reliably identify underlying mechanisms and cross-order effects.

Contribution

It introduces a novel analysis of cross-order behaviors in higher-order networks and demonstrates the effectiveness of simple information-theoretic measures in identifying underlying dynamical rules.

Findings

Synergy is the most reliable indicator of the true underlying mechanism.

Cross-order behaviors can emerge without direct structural correlations.

Simpler measures effectively identify higher-order mechanisms.

Abstract

Recent studies have shown that novel collective behaviors emerge in complex systems due to higher-order interactions. However, the way in which the structural correlations of these interactions shape such behaviors remains a significant gap in current research. To address this, we use signatures of higher-order behaviors (HOBs) to identify the underlying dynamical rules, or higher-order mechanisms (HOMs). In this work, we compare several HOB measures derived from information theory. Utilizing a simplicial SIS contagion model, we demonstrate that simpler, computationally efficient measures can serve as robust indicators of HOMs. We uncover the novel phenomenon of cross-order induced behaviors, where behavioral signatures emerge at interaction orders where no direct mechanism is present. Crucially, these cross-order HOBs are not simply induced by structural correlations -- such as nestedness and hyperedge overlap -- but they appear in the neighborhood of any HOM. Among the information-theoretic measures we tested, synergy is the most reliable indicator of the true order where the underlying mechanism is at play. These findings offer new insights into the relationship between the network structure and observed dynamics of higher-order systems.