While many statistical models and methods are now available for network analysis,
resampling of network data remains a challenging problem. Cross-validation is a useful …

Identifying and tracking community structures in complex networks are one of the
cornerstones of network studies, spanning multiple disciplines, from statistics to machine …

Community detection is a fundamental statistical problem in network data analysis. In this
paper, we present a polynomial time two-stage method that provably achieves optimal …

The stochastic block model is a popular tool for studying community structures in network
data. We develop a goodness-of-fit test for the stochastic block model. The test statistic is …

Real‐world networks usually have community structure, that is, nodes are grouped into
densely connected communities. Community detection is one of the most popular and best …

Likelihood-based model selection for stochastic block models Page 1 The Annals of
Statistics 2017, Vol. 45, No. 2, 500–528 DOI: 10.1214/16-AOS1457 © Institute of …

The stochastic block model (SBM) and its variants have been a popular tool for analyzing
large network data with community structures. In this article, we develop an efficient network …

We have collected and cleaned two network data sets: Coauthorship and Citation networks
for statisticians. The data sets are based on all research papers published in four of the top …

Community structure in complex networks has been proven to be valuable in a variety of
fields, such as biology, social media, health, etc. Researchers have investigated a significant …

We review the class of continuous latent space (statistical) models for network data, paying
particular attention to the role of the geometry of the latent space. In these models, the …