Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

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 …

Several research studies have shown that complex networks modeling real-world
phenomena are characterized by striking properties:(i) they are organized according to …

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 …

Across many scientific domains, there is a common need to automatically extract a simplified
view or coarse-graining of how a complex system's components interact. This general task is …

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 …

Community structure is one of the most relevant features encountered in numerous real-
world applications of networked systems. Despite the tremendous effort of a large …

Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …

Community detection is challenging when the network structure is estimated with
uncertainty. Dynamic networks present additional challenges but also add information …

Recent theoretical work on the modeling of network structure has focused primarily on
networks that are static and unchanging, but many real-world networks change their …