Abstract: Human interaction and communication has become one of the essential features of social life. Individuals’ preference may be influenced strongly by those of their peers or friends in a social network. So far, traditional ranking models do not account for such social network dependency. In this talk, I introduce a new class of models called social order statistics (SOS) models to learn ranking data in social networks. The new models combine the order statistics models and spatial autoregressive models to account for social dependencies among the individuals. A flexible formulation of weight matrices in the spatial model is adopted to provide diverse network effects among the individuals for different items. Efficient and scalable MCMC algorithms are developed to perform Bayesian inference in a parallel manner for large networks with even a few thousand nodes. Simulation and empirical studies demonstrate the usefulness of our proposed inference procedures and reveal that social network effects could be different for individuals’ preferences towards different items in a social relationship.