Description

The package provides model fitting and estimation functions for some popular random network models. More importantly, it implements a general cross-validation framework for model selection and parameter tuning called ECV. Several other model selection methods are also included.

Details

Author(s)

Tianxi Li, Elizaveta Levina, Ji Zhu

Maintainer: Tianxi Li <tianxili@umich.edu>

References

T. Li, E. Levina, and J. Zhu. Network cross-validation by edge sampling. arXiv preprint arXiv:1612.04717, 2016.

K. Chen and J. Lei. Network cross-validation for determining the number of communities in network data. Journal of the American Statistical Association, 113(521):241-251, 2018.

K. Rohe, S. Chatterjee, and B. Yu. Spectral clustering and the high-dimensional stochastic blockmodel. The Annals of Statistics, pages 1878-1915, 2011.

A. A. Amini, A. Chen, P. J. Bickel, and E. Levina. Pseudo-likelihood methods for community detection in large sparse networks. The Annals of Statistics, 41(4):2097-2122, 2013.

Qin, T. & Rohe, K. Regularized spectral clustering under the degree-corrected stochastic blockmodel Advances in Neural Information Processing Systems, 2013, 3120-3128

J. Lei and A. Rinaldo. Consistency of spectral clustering in stochastic block models. The Annals of Statistics, 43(1):215-237, 2014.

C. M. Le, E. Levina, and R. Vershynin. Concentration and regularization of random graphs. Random Structures & Algorithms, 2017.

S. J. Young and E. R. Scheinerman. Random dot product graph models for social networks. In International Workshop on Algorithms and Models for the Web-Graph, pages 138-149. Springer, 2007.

C. M. Le and E. Levina. Estimating the number of communities in networks by spectral methods. arXiv preprint arXiv:1507.00827, 2015.

Zhang, Y.; Levina, E. & Zhu, J. Estimating network edge probabilities by neighbourhood smoothing Biometrika, Oxford University Press, 2017, 104, 771-783

B. Karrer and M. E. Newman. Stochastic blockmodels and community structure in networks. Physical Review E, 83(1):016107, 2011.

Wang, Y. R. & Bickel, P. J. Likelihood-based model selection for stochastic block models The Annals of Statistics, Institute of Mathematical Statistics, 2017, 45, 500-528

Gao, C.; Ma, Z.; Zhang, A. Y. & Zhou, H. H. Achieving optimal misclassification proportion in stochastic block models The Journal of Machine Learning Research, JMLR. org, 2017, 18, 1980-2024