Graphical abstract Abstract Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolutionary conservation information for reducing the high false positive rate. However, there is a lot of room for improvement, as researchers estimate high false positive and false negative rates for current prediction techniques. In this study, we propose that the context of a putative miRNA target in a protein–protein interaction (PPI) network can be used as an additional filter in a computational miRNA prediction algorithm. We compute several graph theoretic measures on a PPI network of a target organism as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. Our results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, network context correlates better with being a target compared to total context score, a sequence based score provided by TargetScan.