Multilayer Stochastic Block

Multilayer stochastic block models (MSBMs) are statistical frameworks used to analyze complex networks with multiple interconnected layers, aiming to identify community structures within and across these layers. Current research focuses on developing efficient algorithms, such as variational Bayesian EM and spectral clustering methods, often within a mixture model framework to handle heterogeneity in layer structures. These models find applications in diverse fields, including image processing, food trade network analysis, and privacy-preserving community detection in distributed networks, offering powerful tools for understanding complex relationships in large datasets.