154 citations · 195 across the 13 of their papers we have counts for
13 papers
Latent space models for grouped multiplex networks
Alexander Kagan, Peter W. MacDonald, Elizaveta Levina +1
Complex multilayer network datasets have become ubiquitous in various applications, including neuroscience, social sciences, economics, and genetics. Notable examples include brain…
Flexible Modeling of Information Diffusion on Networks with Statistical Guarantees
Alexander Kagan, Elizaveta Levina, Ji Zhu
Modeling information spread through a network is one of the key problems of network analysis, with applications in a wide array of areas such as marketing and public health. Most a…
Mesoscale two-sample testing for networks
Peter W. MacDonald, Elizaveta Levina, Ji Zhu
Networks arise naturally in many scientific fields as a representation of pairwise connections. Statistical network analysis has most often considered a single large network, but i…
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…
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…
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…
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…
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…
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…
Link prediction for partially observed networks
Yunpeng Zhao, Elizaveta Levina, Ji Zhu
Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative example…
Sparse Ising Models with Covariates
Jie Cheng, Elizaveta Levina, Pei Wang +1
There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additi…
A new approach to Cholesky-based covariance regularization in high dimensions
Adam J. Rothman, Elizaveta Levina, Ji Zhu
In this paper we propose a new regression interpretation of the Cholesky factor of the covariance matrix, as opposed to the well known regression interpretation of the Cholesky fac…
Sparse estimation of large covariance matrices via a nested Lasso penalty
Elizaveta Levina, Adam Rothman, Ji Zhu
The paper proposes a new covariance estimator for large covariance matrices when the variables have a natural ordering. Using the Cholesky decomposition of the inverse, we impose a…