Advances in Network Clustering and Blockmodeling

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 developing a framework to extract the large‐scale structure of networks while avoiding both overfitting and underfitting, and doing so in a manner that is analytically tractable and computationally efficient. The Bayesian inference approach provides a methodologically correct answer to the very central question in network analysis of whether patterns of large‐scale structure can in fact be supported by statistical evidence. Besides this practical aspect, it also opens a window into the fundamental limits of network analysis itself, giving readers a theoretical underpinning we can use to understand more about the nature of network systems. The chapter shows how inferring the SBM can be used to predict missing and spurious links and also sheds light on the fundamental limitations of the detectability of modular structures in networks.