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Mixed Membership Stochastic Block Models for Relational Data with Application to Protein-Protein Interactions

Modeling relational data is an important problem for modern data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patterns, and the degree of membership of objects to groups. Our model explains the data using a small set of parameters that can be reliably estimated with an efficient inference algorithm. In our approach, the set of probabilistic assumptions may be tailored to a specific application domain in order to incorporate intuitions and/or semantics of interest. We demonstrate our methods on simulated data, where they outperform spectral clustering techniques, and we apply our model to a data set of protein-to-protein interactions, to reveal proteins’ diverse functional roles.