46 citations · 89 across the 6 of their papers we have counts for
10 papers
Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes
Xuezhen Li, Can M. Le
Variational inference has been widely used in machine learning literature to fit various Bayesian models. In network analysis, this method has been successfully applied to solve th…
Network Estimation by Mixing: Adaptivity and More
Tianxi Li, Can M. Le
Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network's underlying connect…
Linear regression and its inference on noisy network-linked data
Can M. Le, Tianxi Li
Linear regression on network-linked observations has been an essential tool in modeling the relationship between response and covariates with additional network structures. Previou…
Concentration of random graphs and application to community detection
Can M. Le, Elizaveta Levina, Roman Vershynin
Random matrix theory has played an important role in recent work on statistical network analysis. In this paper, we review recent results on regimes of concentration of random grap…
Edge sampling using network local information
Can M. Le
Edge sampling is an important topic in network analysis. It provides a natural way to reduce network size while retaining desired features of the original network. Sampling methods…
Estimating a network from multiple noisy realizations
Can M. Le, Keith Levin, Elizaveta Levina
Complex interactions between entities are often represented as edges in a network. In practice, the network is often constructed from noisy measurements and inevitably contains som…
Estimating the number of communities in networks by spectral methods
Can M. Le, Elizaveta Levina
Community detection is a fundamental problem in network analysis with many methods available to estimate communities. Most of these methods assume that the number of communities is…
Concentration and regularization of random graphs
Can M. Le, Elizaveta Levina, Roman Vershynin
This paper studies how close random graphs are typically to their expectations. We interpret this question through the concentration of the adjacency and Laplacian matrices in the…
Sparse random graphs: regularization and concentration of the Laplacian
Can M. Le, Elizaveta Levina, Roman Vershynin
We study random graphs with possibly different edge probabilities in the challenging sparse regime of bounded expected degrees. Unlike in the dense case, neither the graph adjacenc…
Optimization via Low-rank Approximation for Community Detection in Networks
Can M. Le, Elizaveta Levina, Roman Vershynin
Community detection is one of the fundamental problems of network analysis, for which a number of methods have been proposed. Most model-based or criteria-based methods have to sol…