Abstract Selecting discriminative features in positive unlabelled (PU) learning tasks is a challenging problem due to lack of negative class information. Traditional supervised and semi-supervised feature selection methods are not able to be applied directly in this scenario, and unsupervised feature selection algorithms are designed to handle unlabelled data while neglecting the available information from positive class. To leverage the partially observed positive class information, we propose to encode the weakly supervised information in PU learning tasks into pairwise constraints between training instances. Violation of pairwise constraints are measured and incorporated into a partially supervised graph embedding model. Extensive experiments on different benchmark databases and a real-world cyber security application demonstrate the effectiveness of our algorithm.