Many types of data are generated by a process that depends on some grouping or community structure of the elements. In a lot of cases, one is interested in obtaining this community structure using the observed data. This thesis examines the use of variational inference methods to estimate the community structure, modelling the data generation process using stochastic block models. Included is the review of two algorithms, the assumptions they are based on, and theoretical properties they exhibit. In addition, a generalization of one of the algorithms is also derived. Finally, numerical experiments are performed to characterize the accuracy obtained in the community recovery problem. The results show that even with random initializations, these algorithms perform well and reasonably quickly, making them a good choice for real-life problems.
