Frequency Heterogeneity can Promote Order yet Undermine Stability in Kuramoto Networks with Higher-Order Interactions
Zheng Wang, Jinjie Zhu, Wenchang Qi, Xianbin Liu

TL;DR
This paper shows that in Kuramoto networks with higher-order interactions, moderate frequency heterogeneity can enhance global order by restructuring attractor landscapes, despite generally destabilizing individual states.
Contribution
It reveals the dual role of frequency heterogeneity in promoting order through basin restructuring while destabilizing linear stability in higher-order oscillator networks.
Findings
Moderate heterogeneity increases the order parameter in strong triadic coupling.
Heterogeneity shifts attractor competition towards more ordered states.
Linear stability of frequency-locked states decreases with heterogeneity.
Abstract
We investigate the interplay between frequency heterogeneity and higher-order triadic interactions in a ring network of Kuramoto oscillators. While both factors individually disrupt ordered states, their combination produces unexpected collective behavior. In the strong triadic coupling regime, moderate frequency heterogeneity substantially increases the global order parameter, with an optimal heterogeneity strength growing approximately linearly with triadic coupling strength. Basin stability analysis reveals that this order-promoting effect arises from a global restructuring of the attractor landscape: frequency heterogeneity shifts the attractor competition in favor of more ordered configurations. Linear stability analysis of frequency-locked twisted states reveals a competing effect: frequency heterogeneity monotonically erodes linear stability and reduces the probability of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNonlinear Dynamics and Pattern Formation · Chaos control and synchronization · Neural Networks and Reservoir Computing
