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Privacy-preserving communication-efficient spectral clustering for distributed multiple networks

Author

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  • Wu, Shanghao
  • Guo, Xiao
  • Zhang, Hai

Abstract

Multi-layer networks arise naturally in various scientific domains including social sciences, biology, neuroscience, among others. The network layers of a given multi-layer network are commonly stored in a local and distributed fashion because of the privacy, ownership, and communication costs. The literature on community detection based on these data is still limited. This paper proposes a new distributed spectral clustering-based algorithm for consensus community detection of the locally stored multi-layer network. The algorithm is based on the power method. It is communication-efficient by allowing multiple local power iterations before aggregation; and privacy-preserving by incorporating the notion of differential privacy. The convergence rate of the proposed algorithm is studied under the assumption that the multi-layer networks are generated from the multi-layer stochastic block models. Numerical studies show the superior performance of the proposed algorithm over competitive algorithms.

Suggested Citation

  • Wu, Shanghao & Guo, Xiao & Zhang, Hai, 2025. "Privacy-preserving communication-efficient spectral clustering for distributed multiple networks," Computational Statistics & Data Analysis, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:csdana:v:212:y:2025:i:c:s0167947325001069
    DOI: 10.1016/j.csda.2025.108230
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    References listed on IDEAS

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    1. Vishesh Karwa & Pavel N. Krivitsky & Aleksandra B. Slavković, 2017. "Sharing social network data: differentially private estimation of exponential family random-graph models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 481-500, April.
    2. Chang, Jinyuan & Hu, Qiao & Kolaczyk, Eric D. & Yao, Qiwei & Yi, Fengting, 2024. "Edge differentially private estimation in the β-model via jittering and method of moments," LSE Research Online Documents on Economics 122099, London School of Economics and Political Science, LSE Library.
    3. Jing Lei & Kehui Chen & Brian Lynch, 2020. "Consistent community detection in multi-layer network data," Biometrika, Biometrika Trust, vol. 107(1), pages 61-73.
    4. Jing Lei, 2020. "Cross-Validation With Confidence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1978-1997, December.
    5. Jing Lei & Kevin Z. Lin, 2023. "Bias-Adjusted Spectral Clustering in Multi-Layer Stochastic Block Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(544), pages 2433-2445, October.
    6. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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