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Protein-protein interactions (PPI) refer to the associations between proteins and the study of these associations. Several approaches have been used to address the problem of predicting PPI. Some of them are based on biological features extracted from a protein sequence (such as, amino acid composition, GO terms, etc.); others use relational and structural features extracted from the PPI network, which can be represented as a graph. Our approach falls in the second category. We adapt a general approach to graph feature extraction that has previously been applied to collaborative recommendation of friends in social networks. Several structural features are identified based on the PPI graph and used to learn classifiers for predicting new interactions. Two datasets containing Saccharomyces cerevisiae PPI are used to test the proposed approach. Both these datasets were assembled from the Database of Interacting Proteins (DIP). We assembled the first data set directly from DIP in April 2006, while the second data set has been used in previous studies, thus making it easy to compare our approach with previous approaches. Several classifiers are trained using the structural features extracted from the interactions graph. The results show good performance (accuracy, sensitivity and specificity), proving that the structural features are highly predictive with respect to PPI.