The design of an efficient link prediction method is still an open hot issue that has been addressed mostly through topological properties in recent years. Yet, other relevant information such as the node attributes may inform the link prediction task and enhance performances. This paper presents a novel framework for link prediction that combines node attributes and structural properties. Furthermore, the proposed method handles uncertainty that characterizes social network noisy and missing data by embracing the general framework of the belief function theory. An experimental evaluation on real world social network data shows that attribute information improves further the prediction results.