A self-consistent framework with generalized local order parameters is derived for the Kuramoto model with dyadic and triadic interactions on hypergraphs, showing bistability onset depends on eigenvector correlations between dyadic and triadic structures.
and Schaub, Michael T
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
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Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
Bayes-THIS applies sparse Bayesian regression with automatic relevance determination to infer hypergraph structure from dynamical data and proves that Taylor expansions create indistinguishable spurious pairwise terms when higher-order interactions concentrate on nodes lacking lower-order links.
The authors establish consistent and asymptotically normal estimators for parameters in composite birth-death processes via conditional likelihood under a Doob h-transform, plus a test for higher-order mechanisms.
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Self-consistent analysis of the Kuramoto model with higher-order interactions
A self-consistent framework with generalized local order parameters is derived for the Kuramoto model with dyadic and triadic interactions on hypergraphs, showing bistability onset depends on eigenvector correlations between dyadic and triadic structures.
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Learning Dynamic Stability Landscapes in Synchronization Networks
Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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Bayesian hypergraph inference from scarce and noisy dynamical observations
Bayes-THIS applies sparse Bayesian regression with automatic relevance determination to infer hypergraph structure from dynamical data and proves that Taylor expansions create indistinguishable spurious pairwise terms when higher-order interactions concentrate on nodes lacking lower-order links.
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Likelihood-based inference for birth-death processes with composite birth mechanisms
The authors establish consistent and asymptotically normal estimators for parameters in composite birth-death processes via conditional likelihood under a Doob h-transform, plus a test for higher-order mechanisms.