Social networks play a major role in today's society, they have shaped the unfolding of social relationships. To analyze networks dynamics, link prediction i.e., predicting potential new links between actors, is concerned with inspecting networks topology evolution over time. A key issue to be addressed is the imperfection of real world social network data which are usually missing, noisy, or partially observed. This uncertainty is perfectly handled under the general framework of the belief function theory. Here, link prediction is addressed from a supervised learning perspective by extending the evidential k-nearest neighbors approach. Each nearest neighbor represents a source of information concerning new links existence. Overall evidence is pooled via the belief function theory fusion scheme. Experiments are conducted on real social network data where performance is evaluated along with a comparative study. Experiment results confirm the effectiveness of the proposed framework, especially when handling skewness in data.