A graph-based approach for positive and unlabeled learning

•Proposal of a graph-based method for Positive and Unlabeled Learning that uses graph-based strategies in all steps.•Effectively use of unlabeled documents to improve classification performances.•Evaluation of 3 usual graph construction methods and 2 label propagation algorithms.•The proposal achieves better results with statistically significant differences (SSD) compared to state-of-art related approaches.•The proposal achieves better results with SSD than methods based on vector space model and graphs.