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Multiorder Laplacian for synchronization in higher-order networks

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arxiv 2003.09734 v3 pith:3CNV7C2Y submitted 2020-03-21 nlin.AO physics.soc-ph

classification nlin.AOphysics.soc-ph
keywords interactionshigher-orderlaplacianoscillatorsanalyticalframeworkgeneralmulti-order
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Traditionally, interaction systems have been described as networks, where links encode information on the pairwise influences among the nodes. Yet, in many systems, interactions take place in larger groups. Recent work has shown that higher-order interactions between oscillators can significantly affect synchronization. However, these early studies have mostly considered interactions up to 4 oscillators at time, and analytical treatments are limited to the all-to-all setting. Here, we propose a general framework that allows us to effectively study populations of oscillators where higher-order interactions of all possible orders are considered, for any complex topology described by arbitrary hypergraphs, and for general coupling functions. To this scope, we introduce a multi-order Laplacian whose spectrum determines the stability of the synchronized solution. Our framework is validated on three structures of interactions of increasing complexity. First, we study a population with all-to-all interactions at all orders, for which we can derive in a full analytical manner the Lyapunov exponents of the system, and for which we investigate the effect of including attractive and repulsive interactions. Second, we apply the multi-order Laplacian framework to synchronization on a synthetic model with heterogeneous higher-order interactions. Finally, we compare the dynamics of coupled oscillators with higher-order and pairwise couplings only, for a real dataset describing the macaque brain connectome, highlighting the importance of faithfully representing the complexity of interactions in real-world systems. Taken together, our multi-order Laplacian allows us to obtain a complete analytical characterization of the stability of synchrony in arbitrary higher-order networks, paving the way towards a general treatment of dynamical processes beyond pairwise interactions.

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  1. Revealing Higher-Order Interactions in Complex Networks: A U.S. Diplomacy Case Study

    cs.SI 2025-09 conditional novelty 5.0 of 10

    On U.S. diplomatic cables and Senate bills, a non-Markovian random walk on hypergraphs predicts missing and novel group interactions better than pairwise-graph walks.

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