There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …

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 …

This chapter describes the basic variants of the stochastic blockmodel (SBM), and a
consistent Bayesian formulation that allows readers to infer them from data. The focus is on …

Discovering and characterizing the large-scale topological features in empirical networks
are crucial steps in understanding how complex systems function. However, most existing …

Community detection is an important task in network analysis, in which we aim to learn a
network partition that groups together vertices with similar community-level connectivity …

Supplementary material for “Fast communication detetion by SCORE”. Owing to space
constraints, the technical proofs are relegated a supplementary document. The …

A principled approach to characterize the hidden structure of networks is to formulate
generative models and then infer their parameters from data. When the desired structure is …

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 …

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 …

Community detection is one of the most important methodological fields of network science,
and one which has attracted a significant amount of attention over the past decades. This …