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Consistent estimation of the number of communities via regularized network embedding

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  • Mingyang Ren
  • Sanguo Zhang
  • Junhui Wang

Abstract

The network analysis plays an important role in numerous application domains including biomedicine. Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. In this paper, we propose a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector toward its center and pushes similar community centers collapsed with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. Extensive numerical experiments have also been conducted on both synthetic networks and brain functional connectivity network, which demonstrate the superior performance of the proposed method compared with existing alternatives.

Suggested Citation

  • Mingyang Ren & Sanguo Zhang & Junhui Wang, 2023. "Consistent estimation of the number of communities via regularized network embedding," Biometrics, The International Biometric Society, vol. 79(3), pages 2404-2416, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2404-2416
    DOI: 10.1111/biom.13815
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    References listed on IDEAS

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    1. Peter J. Bickel & Purnamrita Sarkar, 2016. "Hypothesis testing for automated community detection in networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 253-273, January.
    2. Lan Wang & Bo Peng & Jelena Bradic & Runze Li & Yunan Wu, 2020. "Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1726-1729, December.
    3. Jianwei Hu & Hong Qin & Ting Yan & Yunpeng Zhao, 2020. "Corrected Bayesian Information Criterion for Stochastic Block Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1771-1783, December.
    4. Jianqing Fan & Cong Ma & Kaizheng Wang, 2020. "Comment on “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1720-1725, December.
    5. Kehui Chen & Jing Lei, 2018. "Network Cross-Validation for Determining the Number of Communities in Network Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 241-251, January.
    6. Bing Li & Eftychia Solea, 2018. "A Nonparametric Graphical Model for Functional Data With Application to Brain Networks Based on fMRI," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1637-1655, October.
    7. Jiadong Ji & Yong He & Lei Liu & Lei Xie, 2021. "Brain connectivity alteration detection via matrix‐variate differential network model," Biometrics, The International Biometric Society, vol. 77(4), pages 1409-1421, December.
    8. Lan Wang & Bo Peng & Jelena Bradic & Runze Li & Yunan Wu, 2020. "A Tuning-free Robust and Efficient Approach to High-dimensional Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1700-1714, December.
    9. Xiudi Li & Ali Shojaie, 2020. "Discussion of “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1717-1719, December.
    10. Shujie Ma & Jian Huang, 2017. "A Concave Pairwise Fusion Approach to Subgroup Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 410-423, January.
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