Emergent synchrony in oscillator networks with adaptive arbitrary-order interactions

TL;DR

This paper introduces an adaptive hypergraph Kuramoto model to analyze how higher-order interactions influence synchronization, providing exact analytical results and numerical validation.

Contribution

It formulates a novel hypergraph-based Kuramoto model with arbitrary-order interactions and derives exact order parameter dynamics in the thermodynamic limit.

Findings

Analytical expressions for collective dynamics are derived.

Numerical simulations confirm analytical predictions.

Finite-size fluctuations affect long-term dynamics.

Abstract

Dynamics of complex systems are often driven by interactions that extend beyond pairwise links, underscoring the need to establish a correspondence between interpretable system parameters and emergent phenomena in hypergraph-based networks. The current work formulates an adaptive Kuramoto model that incorporates hyperedges of arbitrary order and explores their effects on synchronization. By deriving the exact order parameter dynamics in the thermodynamic limit, analytical expressions governing the collective dynamics are obtained. Subsequent numerics confirm the analytical predictions, in addition to capturing qualitatively different dynamical regimes and phase transitions. Further investigations based on order parameter distributions demonstrate how fluctuations, arising due to finite system size, can influence the long-term system dynamics. These results provide important insights and can have diverse applications, such as designing optimal surgical procedures for drug-resistant epilepsy and identifying the sources of rumours in a social network.