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Minh Tang
Minh Tang
Verified email at ncsu.edu
Title
Cited by
Cited by
Year
A consistent adjacency spectral embedding for stochastic blockmodel graphs
DL Sussman, M Tang, DE Fishkind, CE Priebe
Journal of the American Statistical Association 107 (499), 1119-1128, 2012
2752012
Statistical inference on random dot product graphs: a survey
A Athreya, DE Fishkind, M Tang, CE Priebe, Y Park, JT Vogelstein, ...
The Journal of Machine Learning Research 18 (1), 8393-8484, 2017
1862017
Community detection and classification in hierarchical stochastic blockmodels
V Lyzinski, M Tang, A Athreya, Y Park, CE Priebe
IEEE Transactions on Network Science and Engineering 4 (1), 13-26, 2016
1372016
Universally consistent vertex classification for latent positions graphs
M Tang, DL Sussman, CE Priebe
1272013
A limit theorem for scaled eigenvectors of random dot product graphs
A Athreya, CE Priebe, M Tang, V Lyzinski, DJ Marchette, DL Sussman
Sankhya A 78, 1-18, 2016
1222016
Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown
DE Fishkind, DL Sussman, M Tang, JT Vogelstein, CE Priebe
SIAM Journal on Matrix Analysis and Applications 34 (1), 23-39, 2013
1122013
A semiparametric two-sample hypothesis testing problem for random graphs
M Tang, A Athreya, DL Sussman, V Lyzinski, Y Park, CE Priebe
Journal of Computational and Graphical Statistics 26 (2), 344-354, 2017
1092017
Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
V Lyzinski, DL Sussman, M Tang, A Athreya, CE Priebe
1092014
Consistent latent position estimation and vertex classification for random dot product graphs
DL Sussman, M Tang, CE Priebe
IEEE transactions on pattern analysis and machine intelligence 36 (1), 48-57, 2013
1072013
Locality statistics for anomaly detection in time series of graphs
H Wang, M Tang, Y Park, CE Priebe
IEEE Transactions on Signal Processing 62 (3), 703-717, 2013
1032013
The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
J Cape, M Tang, CE Priebe
992019
Limit theorems for eigenvectors of the normalized Laplacian for random graphs
M Tang, CE Priebe
882018
A nonparametric two-sample hypothesis testing problem for random graphs
M Tang, A Athreya, DL Sussman, V Lyzinski, CE Priebe
88*2017
A statistical interpretation of spectral embedding: the generalised random dot product graph
P Rubin-Delanchy, J Cape, M Tang, CE Priebe
arXiv preprint arXiv:1709.05506, 2017
69*2017
On a two-truths phenomenon in spectral graph clustering
CE Priebe, Y Park, JT Vogelstein, JM Conroy, V Lyzinski, M Tang, ...
Proceedings of the National Academy of Sciences 116 (13), 5995-6000, 2019
602019
A central limit theorem for an omnibus embedding of random dot product graphs
K Levin, A Athreya, M Tang, V Lyzinski, CE Priebe
arXiv preprint arXiv:1705.09355, 2017
49*2017
Statistical inference on errorfully observed graphs
CE Priebe, DL Sussman, M Tang, JT Vogelstein
Journal of Computational and Graphical Statistics 24 (4), 930-953, 2015
472015
Signal-plus-noise matrix models: eigenvector deviations and fluctuations
J Cape, M Tang, CE Priebe
Biometrika 106 (1), 243-250, 2019
442019
On estimation and inference in latent structure random graphs
A Athreya, M Tang, Y Park, CE Priebe
352021
Generalized canonical correlation analysis for classification
C Shen, M Sun, M Tang, CE Priebe
Journal of Multivariate Analysis 130, 310-322, 2014
322014
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Articles 1–20