| Saturated reconstruction of a volume of neocortex N Kasthuri, KJ Hayworth, DR Berger, RL Schalek, JA Conchello, ... Cell 162 (3), 648-661, 2015 | 1088 | 2015 |
| 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 | 339 | 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 | 292 | 2018 |
| 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 | 158 | 2017 |
| Universally consistent vertex classification for latent positions graphs M Tang, DL Sussman, CE Priebe | 150 | 2013 |
| 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 | 145 | 2016 |
| Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding V Lyzinski, DL Sussman, M Tang, A Athreya, CE Priebe | 145 | 2014 |
| 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 | 136 | 2013 |
| A nonparametric two-sample hypothesis testing problem for random graphs M Tang, A Athreya, DL Sussman, V Lyzinski, CE Priebe | 121* | 2017 |
| 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 Arxiv preprint arXiv:1205.0309, 2012 | 119 | 2012 |
| Elements of estimation theory for causal effects in the presence of network interference DL Sussman, EM Airoldi arXiv preprint arXiv:1702.03578, 2017 | 90 | 2017 |
| Spectral clustering for divide-and-conquer graph matching V Lyzinski, DL Sussman, DE Fishkind, H Pao, L Chen, JT Vogelstein, ... Parallel Computing 47, 70-87, 2015 | 61 | 2015 |
| 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 | 55 | 2015 |
| Connectome Smoothing via Low-rank Approximations R Tang, M Ketcha, A Badea, ED Calabrese, DS Margulies, JT Vogelstein, ... IEEE transactions on medical imaging, 2018 | 41 | 2018 |
| Matched filters for noisy induced subgraph detection DL Sussman, Y Park, CE Priebe, V Lyzinski IEEE transactions on pattern analysis and machine intelligence 42 (11), 2887 …, 2019 | 40 | 2019 |
| Association between visceral adiposity and colorectal polyps on CT colonography RM Summers, J Liu, DL Sussman, AJ Dwyer, B Rehani, PJ Pickhardt, ... American Journal of Roentgenology 199 (1), 48-57, 2012 | 39 | 2012 |
| Causal inference under network interference with noise W Li, DL Sussman, ED Kolaczyk arXiv preprint arXiv:2105.04518, 2021 | 29 | 2021 |
| Empirical Bayes estimation for the stochastic blockmodel S Suwan, DS Lee, R Tang, DL Sussman, M Tang, CE Priebe | 28 | 2016 |
| Fully automated adipose tissue measurement on abdominal CT J Yao, DL Sussman, RM Summers Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and …, 2011 | 28 | 2011 |
| Computing scalable multivariate glocal invariants of large (brain-) graphs D Mhembere, WG Roncal, D Sussman, CE Priebe, R Jung, S Ryman, ... 2013 IEEE Global Conference on Signal and Information Processing, 297-300, 2013 | 23 | 2013 |
Carey E. PriebeProfessor of Applied Mathematics and Statistics, Johns Hopkins University在 jhu.edu 的電子郵件地址已通過驗證
