| Spectral clustering and the high-dimensional stochastic blockmodel K Rohe, S Chatterjee, B Yu | 1097 | 2011 |
| Regularized spectral clustering under the degree-corrected stochastic blockmodel T Qin, K Rohe Advances in neural information processing systems 26, 2013 | 372 | 2013 |
| Fast and accurate detection of evolutionary shifts in Ornstein–Uhlenbeck models M Khabbazian, R Kriebel, K Rohe, C Ané Methods in Ecology and Evolution 7 (7), 811-824, 2016 | 224 | 2016 |
| Fantope projection and selection: A near-optimal convex relaxation of sparse PCA VQ Vu, J Cho, J Lei, K Rohe Advances in neural information processing systems 26, 2013 | 207 | 2013 |
| Covariate-assisted spectral clustering N Binkiewicz, JT Vogelstein, K Rohe Biometrika 104 (2), 361-377, 2017 | 189 | 2017 |
| Co-clustering directed graphs to discover asymmetries and directional communities K Rohe, T Qin, B Yu Proceedings of the National Academy of Sciences 113 (45), 12679-12684, 2016 | 173* | 2016 |
| Attention and amplification in the hybrid media system: The composition and activity of Donald Trump’s Twitter following during the 2016 presidential election Y Zhang, C Wells, S Wang, K Rohe New Media & Society 20 (9), 3161-3182, 2018 | 141 | 2018 |
| Understanding regularized spectral clustering via graph conductance Y Zhang, K Rohe Advances in Neural Information Processing Systems 31, 2018 | 115 | 2018 |
| Preconditioning the lasso for sign consistency J Jia, K Rohe | 82* | 2015 |
| Social media public opinion as flocks in a murmuration: Conceptualizing and measuring opinion expression on social media Y Zhang, F Chen, K Rohe Journal of Computer-Mediated Communication 27 (1), zmab021, 2022 | 44 | 2022 |
| The lasso under poisson-like heteroscedasticity J Jia, K Rohe, B Yu Statistica Sinica, 0 | 42* | |
| A critical threshold for design effects in network sampling K Rohe | 37* | 2019 |
| Novel sampling design for respondent-driven sampling M Khabbazian, B Hanlon, Z Russek, K Rohe | 29 | 2017 |
| The blessing of transitivity in sparse and stochastic networks K Rohe, T Qin arXiv preprint arXiv:1307.2302, 2013 | 21 | 2013 |
| Central limit theorems for network driven sampling X Li, K Rohe | 19 | 2017 |
| The highest dimensional stochastic blockmodel with a regularized estimator K Rohe, T Qin, H Fan Statistica Sinica, 1771-1786, 2014 | 19 | 2014 |
| Estimating graph dimension with cross-validated eigenvalues F Chen, S Roch, K Rohe, S Yu arXiv preprint arXiv:2108.03336, 2021 | 17 | 2021 |
| A new basis for sparse principal component analysis F Chen, K Rohe arXiv preprint arXiv:2007.00596, 2020 | 17* | 2020 |
| Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise J Cho, D Kim, K Rohe Statistica Sinica, 1921-1948, 2017 | 17 | 2017 |
| Targeted sampling from massive block model graphs with personalized PageRank F Chen, Y Zhang, K Rohe Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2020 | 13 | 2020 |
Bin YUProfessor of Statistics and EECS, UC Berkeley在 stat.berkeley.edu 的电子邮件经过验证
