The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

Recovering low-rank structures via eigenvector perturbation analysis is a common problem
in statistical machine learning, such as in factor analysis, community detection, ranking …

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 …

Supplement to “Mimimax rates of community detection in stochastic block models”. In the
supplement 31, we provide proofs of Lemma 5.2, Propositions 5.1 and 5.2. We also provide …

The binary symmetric stochastic block model deals with a random graph of n vertices
partitioned into two equal-sized clusters, such that each pair of vertices is independently …

Community detection in degree-corrected block models Page 1 The Annals of Statistics 2018,
Vol. 46, No. 5, 2153–2185 https://doi.org/10.1214/17-AOS1615 © Institute of Mathematical …

Inference problems with conjectured statistical-computational gaps are ubiquitous
throughout modern statistics, computer science, statistical physics and discrete probability …

On semidefinite relaxations for the block model Page 1 The Annals of Statistics 2018, Vol. 46,
No. 1, 149–179 https://doi.org/10.1214/17-AOS1545 © Institute of Mathematical Statistics …

Community detection in graphs is the problem of finding groups of vertices which are more
densely connected than they are to the rest of the graph. This problem has a long history, but …