activity
20082020
most citedSparse estimation of large covariance matrices via a nested Lasso penalty

154 citations · 179 across the 5 of their papers we have counts for

collaborators

10 papers

stat.ME2020

Latent space models for multiplex networks with shared structure

Peter W. MacDonald, Elizaveta Levina, Ji Zhu

Latent space models are frequently used for modeling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product gra…

stat.ME2020

Fast Network Community Detection with Profile-Pseudo Likelihood Methods

Jiangzhou Wang, Jingfei Zhang, Binghui Liu +2

The stochastic block model is one of the most studied network models for community detection. It is well-known that most algorithms proposed for fitting the stochastic block model…

stat.ME2020

Community models for networks observed through edge nominations

Tianxi Li, Elizaveta Levina, Ji Zhu

Communities are a common and widely studied structure in networks, typically under the assumption that the network is fully and correctly observed. In practice, network data are of…

stat.ML2019

High-dimensional Gaussian graphical model for network-linked data

Tianxi Li, Cheng Qian, Elizaveta Levina +1

Graphical models are commonly used to represent conditional dependence relationships between variables. There are multiple methods available for exploring them from high-dimensiona…

stat.CO2018

Link prediction for egocentrically sampled networks

Yun-Jhong Wu, Elizaveta Levina, Ji Zhu

Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes. In practice, especially for social networks, the d…

stat.ME201710 cited

Generalized linear models with low rank effects for network data

Yun-Jhong Wu, Elizaveta Levina, Ji Zhu

Networks are a useful representation for data on connections between units of interests, but the observed connections are often noisy and/or include missing values. One common appr…