Evidence map›Paper›PMID 42850692›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Analytical Prediction of Critical Transitions in Oscillator Networks With Non-Local Links.

Qiang Li, Chumin Sun, David Waxman, Renaud Lambiotte, Jie Sun, Peng Ji

Abstract read
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In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Qiang LiInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0005-3755-0297
Chumin SunTheory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co., Ltd., Sha Tin, Hong Kong.ORCID https://orcid.org/0000-0002-0139-9055
David WaxmanInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0001-9093-2108
Renaud LambiotteMathematical Institute, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-0583-4595
Jie SunTheory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co., Ltd., Sha Tin, Hong Kong.ORCID https://orcid.org/0000-0003-2553-1804
Peng JiInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0002-3225-8431

Funding

Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project 2021ZD0204500Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project 2021ZD0204504National Natural Science Foundation of China 1240503National Natural Science Foundation of China 12504534National Natural Science Foundation of China 62576108Shanghai Magnolia Award-Pujiang Program 24PJD005
6 · The paper itself

Abstract

A wide range of dynamical systems can be modeled as oscillators coupled through an interaction network. Such frameworks have been widely used to explore the emergence of collective phenomena across diverse applications. Here, we develop an analytical framework for oscillator-based Ising systems with Kuramoto-type pairwise coupling and a second harmonic term. Spectral dimension reduction identifies a stability boundary governed by the balance between global coupling and second-harmonic strength, while a mean-field Fokker-Planck analysis predicts the transition between single-cluster and two-cluster stationary states and clarifies the role of noise. Low-energy Ising configurations are predominantly obtained when the second harmonic term is strong relative to noise. Partition-function analysis further shows that, for the ring-based topology, links to third neighbors yield more desired solutions than the other added non-local connections. Applications to graph coloring and MaxCut demonstrate the relevance of these predictions to oscillator-based Ising optimization. These results provide a predictive framework for understanding how dynamical parameters, noise, and network structure jointly shape collective states and low-energy configurations.

Indexed as

complex networkdynamical systemdynamical systems theorykuramoto modelstatistical physics

Identifiers

PMID42850692

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.