research
∙
05/10/2023
An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference
This paper provides a selective review of the statistical network analys...
share
research
∙
03/17/2023
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS
The recent explosion of genetic and high dimensional biobank and 'omic' ...
research
∙
02/08/2022
Spectral embedding and the latent geometry of multipartite networks
Spectral embedding finds vector representations of the nodes of a networ...
research
∙
08/23/2020
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
This paper considers the graph signal processing problem of anomaly dete...
research
∙
07/04/2020
On identifying unobserved heterogeneity in stochastic blockmodel graphs with vertex covariates
Both observed and unobserved vertex heterogeneity can influence block st...
research
∙
08/18/2019
Spectral inference for large Stochastic Blockmodels with nodal covariates
In many applications of network analysis, it is important to distinguish...
research
∙
06/24/2019
Inference for multiple heterogeneous networks with a common invariant subspace
The development of models for multiple heterogeneous network data is of ...
research
∙
08/23/2018
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without an...
research
∙
08/22/2018
Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions
We propose a Bayesian methodology for estimating spiked covariance matri...
research
∙
08/14/2018
On spectral embedding performance and elucidating network structure in stochastic block model graphs
Statistical inference on graphs often proceeds via spectral methods invo...
research
∙
02/01/2018
