Degree Normalization Reshapes Collective Synchronization in Adaptive Kuramoto Networks

Collective dynamics in adaptive oscillator networks are shaped by both network structure and coupling-adaptation mechanisms, and separating the contributions of the two to synchronization is often difficult. We study a Kuramoto network with degree normalization and adaptive Hebbian weights. Introducing the degree-normalization exponent α, we examine how it affects the final synchronization order parameter Rfinal, and we separate the two effects by turning the mechanism off and running a paired control. We find that the increase of Rfinal with α is driven primarily by degree normalization rather than by the adaptive weight dynamics, and that it survives a control in which wij ≡ 1 is held fixed. The structural coupling budget takes the form Kieff = K (ki/N)1−α , whose dependence on node degree weakens as α grows; the coupling heterogeneity of the fixedweight control follows from the analytic degree distribution, and the numerical results match the analytic ones to machine precision. The effect of the adaptive weights on Rfinal, by contrast, carries a network-dependent sign: for two of the three network types it is negative at every sampled α , while for the third it crosses zero near α ≈ 0. 55 and turns positive. Because the three network types differ simultaneously in degree distribution, spatial embedding and average path length, this sign change cannot yet be attributed to degree heterogeneity alone. Mechanism-off controls, paired comparisons and analytic verification together resolve the relative roles of degree normalization and adaptive weights in the models studied here.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-05
DOI
https://doi.org/10.5281/zenodo.23154182
Primary Topic
Nonlinear Dynamics and Pattern Formation
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Degree Normalization Reshapes Collective Synchronization in Adaptive Kuramoto Networks

Feng Dai, Zhaoyi Zhang, Rangxiong Liu, Dongsheng He
Zenodo (CERN European Organization for Nuclear Research)
Nonlinear Dynamics and Pattern Formation
preprint

Degree Normalization Reshapes Collective Synchronization in Adaptive Kuramoto Networks

Feng Dai, Zhaoyi Zhang, Rangxiong Liu, Dongsheng He
preprint en

Abstract

Collective dynamics in adaptive oscillator networks are shaped by both network structure and coupling-adaptation mechanisms, and separating the contributions of the two to synchronization is often difficult. We study a Kuramoto network with degree normalization and adaptive Hebbian weights. Introducing the degree-normalization exponent α, we examine how it affects the final synchronization order parameter Rfinal, and we separate the two effects by turning the mechanism off and running a paired control. We find that the increase of Rfinal with α is driven primarily by degree normalization rather than by the adaptive weight dynamics, and that it survives a control in which wij ≡ 1 is held fixed. The structural coupling budget takes the form Kieff = K (ki/N)1−α , whose dependence on node degree weakens as α grows; the coupling heterogeneity of the fixedweight control follows from the analytic degree distribution, and the numerical results match the analytic ones to machine precision. The effect of the adaptive weights on Rfinal, by contrast, carries a network-dependent sign: for two of the three network types it is negative at every sampled α , while for the third it crosses zero near α ≈ 0. 55 and turns positive. Because the three network types differ simultaneously in degree distribution, spatial embedding and average path length, this sign change cannot yet be attributed to degree heterogeneity alone. Mechanism-off controls, paired comparisons and analytic verification together resolve the relative roles of degree normalization and adaptive weights in the models studied here.

Zenodo (CERN European Organization for Nuclear Research)
Guangzhou Railway Polytechnic (CN), Huazhong University of Science and Technology (CN)
Nonlinear Dynamics and Pattern Formation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Degree Normalization Reshapes Collective Synchronization in Adaptive Kuramoto Networks — Feng Dai, Zhaoyi Zhang, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS