Asymptotically efficient estimators for stochastic blockmodels: The naive MLE, the rank-constrained MLE, and the spectral estimator-Reference-Cited by-同舟云学术

Asymptotically efficient estimators for stochastic blockmodels: The naive MLE, the rank-constrained MLE, and the spectral estimator

Published:2022-05-01 Issue:2 Volume:28 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Tang Minh¹,Cape Joshua²,Priebe Carey E.³

Affiliation:

1. Department of Statistics, North Carolina State University, Raleigh, NC, USA

2. Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA

3. Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference60 articles.

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2. Abbe, E., Bandeira, A.S. and Hall, G. (2016). Exact recovery in the stochastic block model. IEEE Trans. Inf. Theory 62 471–487.

3. Airoldi, E.M., Blei, D.M., Fienberg, S.E. and Xing, E.P. (2008). Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9 1981–2014.

4. Athreya, A., Priebe, C.E., Tang, M., Lyzinski, V., Marchette, D.J. and Sussman, D.L. (2016). A limit theorem for scaled eigenvectors of random dot product graphs. Sankhya A 78 1–18.

5. Banerjee, D. and Ma, Z. (2017). Optimal hypothesis testing for stochastic blockmodels with growing degrees. Preprint. Available at https://arxiv.org/abs/1705.05305.

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