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 | 300 | 2012 |

Statistical inference on random dot product graphs: a survey A Athreya, DE Fishkind, M Tang, CE Priebe, Y Park, JT Vogelstein, ... Journal of Machine Learning Research 18 (226), 1-92, 2018 | 232 | 2018 |

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 | 154 | 2016 |

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 | 138 | 2016 |

Universally consistent vertex classification for latent positions graphs M Tang, DL Sussman, CE Priebe | 134 | 2013 |

The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics J Cape, M Tang, CE Priebe | 132 | 2019 |

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 | 129 | 2017 |

Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding V Lyzinski, DL Sussman, M Tang, A Athreya, CE Priebe | 126 | 2014 |

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 | 115 | 2013 |

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 | 114 | 2013 |

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 | 114 | 2013 |

Limit theorems for eigenvectors of the normalized Laplacian for random graphs M Tang, CE Priebe | 109 | 2018 |

A statistical interpretation of spectral embedding: The generalised random dot product graph P Rubin-Delanchy, J Cape, M Tang, CE Priebe Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022 | 107* | 2022 |

A nonparametric two-sample hypothesis testing problem for random graphs M Tang, A Athreya, DL Sussman, V Lyzinski, CE Priebe | 100* | 2017 |

A central limit theorem for an omnibus embedding of multiple random dot product graphs K Levin, A Athreya, M Tang, V Lyzinski, CE Priebe 2017 IEEE international conference on data mining workshops (icdmw), 964-967, 2017 | 88* | 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 | 77 | 2019 |

Signal-plus-noise matrix models: eigenvector deviations and fluctuations J Cape, M Tang, CE Priebe Biometrika 106 (1), 243-250, 2019 | 61 | 2019 |

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 | 52 | 2015 |

On estimation and inference in latent structure random graphs A Athreya, M Tang, Y Park, CE Priebe | 45 | 2021 |

Supervised dimensionality reduction for big data JT Vogelstein, EW Bridgeford, M Tang, D Zheng, C Douville, R Burns, ... Nature communications 12 (1), 2872, 2021 | 37* | 2021 |