Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

Stochastic blockmodels have been proposed as a tool for detecting community structure in
networks as well as for generating synthetic networks for use as benchmarks. Most …

Networks and network analysis are arguably one of the largest growth areas of the early
twenty-first century in the quantitative sciences. Despite roots in social network analysis …

Consistency of community detection in networks under degree-corrected stochastic block
models Page 1 The Annals of Statistics 2012, Vol. 40, No. 4, 2266–2292 DOI: 10.1214/12-AOS1036 …

Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently,
Chaudhuri et al. and Amini et al. proposed variations on the algorithm that artificially inflate …

This book presents a unified theory of random matrices for applications in machine learning,
offering a large-dimensional data vision that exploits concentration and universality …

Bipartite networks are a common type of network data in which there are two types of
vertices, and only vertices of different types can be connected. While bipartite networks …

In this paper, we examine a spectral clustering algorithm for similarity graphs drawn from a
simple random graph model, where nodes are allowed to have varying degrees, and we …

Robust and computationally feasible community detection in the presence of arbitrary outlier
nodes Page 1 The Annals of Statistics 2015, Vol. 43, No. 3, 1027–1059 DOI: 10.1214/14-AOS1290 …

Convexified modularity maximization for degree-corrected stochastic block models Page 1
The Annals of Statistics 2018, Vol. 46, No. 4, 1573–1602 https://doi.org/10.1214/17-AOS1595 …