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  Community structure-regulation coupling reveals optimal information diffusion

Chen, X., Xie, M., Meng, J., Fang, S., Chen, X., Kurths, J., Nagler, J., Fan, J. (2026): Community structure-regulation coupling reveals optimal information diffusion. - Nature Communications, 17, 4879.
https://doi.org/10.1038/s41467-026-73665-1

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Chen_2026_s41467-026-73665-1.pdf (Publisher version), 2MB
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Chen_2026_s41467-026-73665-1.pdf
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 Creators:
Chen, Xiaojie1, Author
Xie, Meiling1, Author
Meng, Jun1, Author
Fang, Sheng1, Author
Chen, Xiaosong1, Author
Kurths, Jürgen2, Author           
Nagler, Jan1, Author
Fan, Jingfang2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Effective regulation of information diffusion in complex social systems requires balancing containment and intervention cost, yet how network community structure interacts with targeted interventions remains unclear. We develop a community structure-regulation coupling framework (COSREF) that integrates community structure with process-level regulation of transmission and show how their interplay governs diffusion. Tuning two regulation parameters governing within- and cross-community transmission yields three regimes: no, localized, and global diffusion, separated by abrupt transitions. This structure–regulation perspective reveals a low-cost intervention region where small, targeted adjustments contain spread, unifies topology and regulation within a single theoretical setting, and provides general principles for efficiently and robustly regulating modular systems. Analyses of large cross-platform real-world social networks confirm our analytical predictions and simulation results, demonstrating COSREF’s robustness across investigated topologies and its applicability to real information environments.

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Language(s): eng - English
 Dates: 2026-06-022026-06-02
 Publication Status: Finally published
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41467-026-73665-1
MDB-ID: No MDB - stored outside PIK (see locators/paper)
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
OATYPE: Gold Open Access
 Degree: -

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Title: Nature Communications
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 17 Sequence Number: 4879 Start / End Page: - Identifier: Publisher: Nature
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354