Topological cluster synchronization via Dirac spectral programming on directed hypergraphs

TL;DR

This paper introduces a Dirac spectral programming framework that enables programmable topological cluster synchronization in directed hypergraphs, allowing control over complex collective dynamics without altering network structure.

Contribution

The authors develop a novel spectral programming method using a Dirac operator and a tunable mass term to control synchronization patterns in directed hypergraphs.

Findings

Spectral selection determines synchronization patterns in directed hypergraphs.

The framework successfully controls cluster synchronization in empirical higher-order networks.

Simulations confirm the effectiveness of spectral programming in various models.

Abstract

Collective synchronization in complex systems arises from the interplay between topology and dynamics, yet how to design and control such patterns in higher-order networks remains unclear. Here we show that a Dirac spectral programming framework enables programmable topological cluster synchronization on directed hypergraphs. By encoding tail-head hyperedges into a topological Dirac operator and introducing a tunable mass term, we obtain a spectrum whose isolated eigenvalues correspond to distinct synchronization clusters defined jointly on nodes and hyperedges. Selecting a target eigenvalue allows the system to self-organize toward the associated cluster state without modifying the underlying hypergraph structure. Simulations on directed-hypergraph block models and empirical systems--including higher-order contact networks and the ABIDE functional brain network--confirm that spectral selection alone determines the accessible synchronization patterns. Our results establish a general and interpretable route for controlling collective dynamics in directed higher-order systems.