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 …

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 detection is a fundamental problem in network analysis with many methods
available to estimate communities. Most of these methods assume that the number of …

The most widely used techniques for community detection in networks, including methods
based on modularity, statistical inference, and information theoretic arguments, all work by …

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with
good interpretability, expressiveness, generalization, and flexibility, which has become …

Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for
the important task of node clustering has not been fully exploited. In particular, most state-of …

Discovering community structure in complex networks is a mature field since a tremendous
number of community detection methods have been introduced in the literature …

Community detection methods attempt to divide a network into groups of nodes that share
similar properties, thus revealing its large-scale structure. A major challenge when …

Community detection is a fundamental problem in network analysis with many methods
available to estimate communities. Most of these methods assume that the number of …

Criminal networks analysis has attracted several numbers of researchers as network
analysis gained its popularity among professionals and researchers. In this study, we have …