Stochastic Blockmodel Estimation

This lecture by the instructor covers the estimation of the Stochastic Blockmodel, focusing on methods like spectral clustering and network modularity. It delves into the parametric properties of graphs, the likelihood maximization process, and the network modularity for label-vector estimation. The lecture also discusses the Laplacian matrix, eigenvectors, and clustering techniques such as k-means clustering. The presentation emphasizes the importance of understanding the latent blocks in networks and the challenges in recovering them. Various block model estimators and their applications are explored, providing insights into the statistical analysis of network data.