Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vector- valued functions, we establish a new repre- senter theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and show its relevance us- ing numerical experiments on arti cial net- works and two real applications using a very low percentage of labeled data in a transduc- tive setting.