Mixed Membership Stochastic Blockmodels (MMSB) are probabilistic models for relational data, such as social and protein interaction networks. The model extends traditional blockmodels to capture mixed membership latent relational structures, providing object-specific low-dimensional representations. It uses a variational inference algorithm for fast posterior inference and has been applied to social and protein interaction networks. The model allows objects to belong to multiple clusters, capturing different roles they may play in interactions. It accounts for sparsity in data and can handle both symmetric and asymmetric interactions. The MMSB was tested on real-world data, including a friendship network among students and a protein interaction network. The model successfully recovered latent block structures and provided insights into the functional roles of proteins. The results show that the MMSB can reduce data dimensionality while revealing meaningful information about protein functions. The model also demonstrated effectiveness in identifying clusters in social networks, with results comparable to other models. The MMSB is a flexible tool for analyzing relational data, offering a balance between computational efficiency and statistical accuracy.Mixed Membership Stochastic Blockmodels (MMSB) are probabilistic models for relational data, such as social and protein interaction networks. The model extends traditional blockmodels to capture mixed membership latent relational structures, providing object-specific low-dimensional representations. It uses a variational inference algorithm for fast posterior inference and has been applied to social and protein interaction networks. The model allows objects to belong to multiple clusters, capturing different roles they may play in interactions. It accounts for sparsity in data and can handle both symmetric and asymmetric interactions. The MMSB was tested on real-world data, including a friendship network among students and a protein interaction network. The model successfully recovered latent block structures and provided insights into the functional roles of proteins. The results show that the MMSB can reduce data dimensionality while revealing meaningful information about protein functions. The model also demonstrated effectiveness in identifying clusters in social networks, with results comparable to other models. The MMSB is a flexible tool for analyzing relational data, offering a balance between computational efficiency and statistical accuracy.