Topological cluster synchronization via Dirac spectral programming on directed hypergraphs

Topological cluster synchronization via Dirac spectral programming on directed hypergraphs
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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.


💡 Research Summary

The paper introduces a novel framework called Dirac‑Equation Synchronization Dynamics (DESD) that enables programmable cluster synchronization on directed hypergraphs. By representing a directed hypergraph through a degree‑balanced boundary matrix (B) (which captures the oriented flow from tail to head node sets) and constructing the topological Dirac operator (D=\begin{pmatrix}0&B\ B^{\top}&0\end{pmatrix}), the authors obtain a Hamiltonian (H = D + m\gamma) where (\gamma=\mathrm{diag}(I_{|V|},-I_{|E|})) and (m) is a tunable mass term. The mass creates a spectral gap that separates bulk modes from isolated eigenvalues (|E|>m). Each isolated eigenvalue corresponds to a Dirac eigenmode that is strongly localized on a mesoscopic structural feature (a cluster) of the hypergraph.

The dynamics of node phases (\theta) and hyperedge phases (\phi) are governed by
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