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Network cross-validation by edge sampling

Author

Listed:
  • Tianxi Li
  • Elizaveta Levina
  • Ji Zhu

Abstract

Summary While many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper we propose a new network resampling strategy, based on splitting node pairs rather than nodes, that is applicable to cross-validation for a wide range of network model selection tasks. We provide theoretical justification for our method in a general setting and examples of how the method can be used in specific network model selection and parameter tuning tasks. Numerical results on simulated networks and on a statisticians’ citation network show that the proposed cross-validation approach works well for model selection.

Suggested Citation

  • Tianxi Li & Elizaveta Levina & Ji Zhu, 2020. "Network cross-validation by edge sampling," Biometrika, Biometrika Trust, vol. 107(2), pages 257-276.
  • Handle: RePEc:oup:biomet:v:107:y:2020:i:2:p:257-276.
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    File URL: http://hdl.handle.net/10.1093/biomet/asaa006
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    Citations

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    Cited by:

    1. Ding, Yi & Li, Yingying & Liu, Guoli & Zheng, Xinghua, 2024. "Stock co-jump networks," Journal of Econometrics, Elsevier, vol. 239(2).
    2. Su, Wenqing & Guo, Xiao & Chang, Xiangyu & Yang, Ying, 2024. "Spectral co-clustering in multi-layer directed networks," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
    3. Yuan, Quan & Liu, Binghui, 2021. "Community detection via an efficient nonconvex optimization approach based on modularity," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    4. Deng, Jiayi & Huang, Danyang & Ding, Yi & Zhu, Yingqiu & Jing, Bingyi & Zhang, Bo, 2024. "Subsampling spectral clustering for stochastic block models in large-scale networks," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    5. Guo, Xiao & Zhang, Hai & Chang, Xiangyu, 2024. "On the efficacy of higher-order spectral clustering under weighted stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    6. Jesús Arroyo & Elizaveta Levina, 2022. "Overlapping Community Detection in Networks via Sparse Spectral Decomposition," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 1-35, June.
    7. Vainora, J., 2024. "Latent Position-Based Modeling of Parameter Heterogeneity," Cambridge Working Papers in Economics 2455, Faculty of Economics, University of Cambridge.
    8. Yong Cai, 2022. "Linear Regression with Centrality Measures," Papers 2210.10024, arXiv.org.
    9. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. Li Guo & Wolfgang Karl Härdle & Yubo Tao, 2024. "A Time-Varying Network for Cryptocurrencies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 437-456, April.
    11. Wu, Qianyong & Hu, Jiang, 2024. "Two-sample test of stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    12. Watanabe, Chihiro & Suzuki, Taiji, 2021. "Goodness-of-fit test for latent block models," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).

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