randnet-package | R Documentation |

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. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Package: | randnet |

Type: | Package |

Version: | 0.7 |

Date: | 2023-05-11 |

License: | GPL (>= 2) |

Tianxi Li, Elizaveta Levina, Ji Zhu, Can M. Le

Maintainer: Tianxi Li <tianxili@virginia.edu>

T. Li, E. Levina, and J. Zhu. Network cross-validation by edge sampling. Biometrika, 107(2), pp.257-276, 2020.

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

T. Li, E. Levina, and J. Zhu. Community models for networks observed through edge nominations. arXiv preprint arXiv:2008.03652 (2020).

T. Li and C. M. Le, Network Estimation by Mixing: Adaptivity and More. arXiv preprint arXiv:2106.02803, 2021.

R. Miao and T. Li. Informative core identification in complex networks. arXiv preprint arXiv:2101.06388, 2021

Sischka, B. and Kauermann, G., 2022. EM-based smooth graphon estimation using MCMC and spline-based approaches. Social Networks, 68, pp.279-295.

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