We derive a one-dimensional reduction for nonlinear dynamics on simplicial complexes containing both pairwise and triangular (higher-order) interactions. The effective state is defined using a mixed weight determined by the pairwise and triangular degrees of each node. The resulting reduced equation retains two structural coefficients, associated separately with the pairwise and higher-order coupling channels. A fluctuation expansion identifies the closure assumptions underlying the reduction and shows how deviations of individual node states from the effective state contribute to the approximation error. We numerically validate the proposed framework on Gene-regulatory dynamics, the double-well system, and SIS spreading. The states of the reduced model are compared with full-network simulations through coupling-parameter sweeps, steady-state branch calculations, and progressive node-removal experiments on synthetic and real-world networks. The reduced model successfully reproduces the principal transitions and steady-state branches in all three dynamical systems considered. Agreement is strongest for relatively homogeneous networks and deteriorates when structural heterogeneity produces a broader distribution of node states. The closure diagnostics account for this loss of accuracy and indicate when a single effective state is no longer sufficient. The reduction therefore provides a tractable description of resilience in systems with coexisting pairwise and higher-order interactions.
A one-dimensional reduction for dynamical systems on networks with purely higher-order interactions is developed, formulated through an effective higher-order interaction strength, associated with the triangular interactions of the underlying network and the dynamical system's effective state.
Amit Tiwari, C. Hens, Prosenjit Kundu· Proceedings of the Royal Soc...· 1 citation
Across all the realizations, SSM/gSSM consistently outperform classical spectral and mean-field methods in modeling critical transitions at both microscopic and macroscopic scales, establishing SSM-based reduction as a robust, interpretable tool for nonlinear networked systems with broad applicability to epidemiology,...
A bottom-up derivation of higher-order SIS dynamics, building systematically from node-level equations to pairs, triplets, and three-body interactions and provides a principled method of generating new models.
Kevin Teo, Péter L. Simon, I. Z. Kiss· 0 citations
Most complex systems, from the brain to ecosystems and multi-agent networks, display pairwise and higher-order interactions that determine their collective behavior. Yet, the regimes in which one interaction order dominate over another, and the mechanisms by which systems move between such regimes remain poorly underst...
P. Tewarie, Steven Laureys, R. Hindriks· Chaos· 0 citations
A novel large-scale delayed neural network with dual-star architecture, incorporating both second-order and third-order interactions, and the accurate function fitting achieved by the model provides new insights for the design and control of neural networks.
Yue Qiu, Min Xiao, Yonghui Sun et al.· Chaos· 0 citations
It is proved that networks of n nodes with local dimension d can be exactly reduced from n d dimensions to a fixed macroscopic system of dimension m d, where m is the number of fundamental solutions required by the nodal dynamics.
Erik A. Martens, S. Dharmavaram· 1 citation
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