Spectral tools for graphs

Vintage Factor Analysis with Varimax Performs Statistical Inference
Karl Rohe and Muzhe Zeng
arXiv, code

Targeted sampling from massive block model graphs with personalized pagerank
Fan Chen, Yini Zhang, and Karl Rohe
Journal of the Royal Statistical Society: Series B, 2019. code

A note on quickly sampling a sparse matrix with low rank expectation
Karl Rohe, Jun Tao, Xintian Han, and Norbert Binkiewicz
Journal of Machine Learning Research, 2018. code

Understanding regularized spectral clustering via graph conductance
Yilin Zhang and Karl Rohe
NeurIPS 2018

Discovering political topics in facebook discussion threads with graph contextualization
Yilin Zhang, Marie Poux-Berthe, Chris Wells, Karolina Koc-Michalska, Karl Rohe
The Annals of Applied Statistics, 2018. (also listed below)

Covariate-assisted spectral clustering
N Binkiewicz, JT Vogelstein, and K Rohe
Biometrika, 2017.

Co-clustering directed graphs to discover asymmetries and directional communities
Karl Rohe, Tai Qin, and Bin Yu
Proceedings of the National Academy of Sciences, 2016.

Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
Tai Qin and Karl Rohe
NeurIPS, 2013

Spectral clustering and the high dimensional stochastic blockmodel
K Rohe, B Yu, and S Chatterjee
The Annals of Statistics, 2011.

Applied social network analysis

The murmuration (manuscript coming soon)

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, 2017

Discovering political topics in facebook discussion threads with graph contextualization
Yilin Zhang, Marie Poux-Berthe, Chris Wells, Karolina Koc-Michalska, Karl Rohe
The Annals of Applied Statistics, 2018. (also listed above)

Latent factors in student–teacher interaction factor analysis
Thu Le, Daniel Bolt, Eric Camburn, Peter Goff, and Karl Rohe
Journal of Educational and Behavioral Statistics, 2017.
(also listed below)

Sparse PCA

A New Basis for Sparse PCA
Fan Chen and Karl Rohe
(Check arXiv soon)

Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
V Vu, J Cho, J Lei, and K Rohe
NeurIPS, 2013.

Preconditioning the Lasso

Preconditioning to comply with the irrepresentable condition
Jinzhu Jia and Karl Rohe
Electronic Journal of Statistics, 2015.

Preconditioning for classical relationships: a note relating ridge regression and ols p-values to preconditioned sparse penalized regression
Karl Rohe
Stat, 2015.

Respondent Driven Sampling

Network driven sampling; a critical threshold for design effects
Rohe
Annals of Statistics, 2019.

Generalized least squares can overcome the critical threshold in respondent-driven sampling
Sebastien Roch and Karl Rohe
Proceedings of the National Academy of Sciences, 2018.

Novel sampling design for respondent-driven sampling
Mohammad Khabbazian, Bret Hanlon, Zoe Russek, Karl Rohe
Electronic Journal of Statistics, 2017.

Central limit theorems for network driven sampling
Xiao Li, Karl Rohe
Electronic Journal of Statistics, 2017.

Asymptotic seed bias in respondent-driven sampling
Yuling Yan, Bret Hanlon, Sebastien Roch, and Karl Rohe
Electronic Journal of Statistics, 2020.

Low rank matrix completion

Intelligent initialization and adaptive thresholding for iterative matrix completion: Some statistical and algorithmic theory for adaptive-impute
Juhee Cho, Donggyu Kim, and Karl Rohe
Journal of Computational and Graphical Statistics, 2019.
code

Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise
Juhee Cho, Donggyu Kim, and Karl Rohe
Statistica Sinica, 2017.

Latent factors in student–teacher interaction factor analysis
Thu Le, Daniel Bolt, Eric Camburn, Peter Goff, and Karl Rohe
Journal of Educational and Behavioral Statistics, 2017.
(also listed above)