Lecture
This lecture covers the Stochastic Block Model (SBM) and its application in community detection. The SBM defines a probability distribution over graphs with communities, where nodes are grouped into blocks. The lecture discusses the mathematical formulation of SBM, including the definition of parameters like lambda and K, as well as the symmetric nature of the model. It also explores the goal of SBM, which is to estimate the assignment of nodes to groups accurately. The lecture delves into the concept of agreement between the true and estimated group assignments, as well as the Bayesian Inference approach for SBM. It concludes with a discussion on the challenges and convergence properties of SBM.