In this paper we address the problem of link prediction in networked data, which appears in many applications such as social network analysis or recommender systems. Previous studies either consider latent feature based models but disregarding local structure in the network, or focus exclusively on capturing local structure of objects based on latent blockmodels without coupling with latent characteristics of objects. To combine the benefits of previous work, we propose a novel model that can incorporate the effects of latent features of objects and local structure in the network simultaneously. To achieve this, we model the relation graph as a function of both latent feature factors and latent cluster memberships of objects to collectively discover globally predictive intrinsic properties of objects and capture latent block structure in the network to improve prediction performance. Extensive experiments on several real world datasets suggest that our proposed model outperforms the other state of the art approaches for link prediction.