Recent studies have investigated changes in the human brain network organization during the normal aging. A reduction of the connectivity between brain areas was demonstrated by combining neuroimaging technologies and graph theory. Clustering, characteristic path length and small-worldness are key topological measures and they are widely used in literature. In this paper we propose a new methodology that combine advanced techniques of effective connectivity estimation, graph theoretical approach and classification by SVM method. EEG signals recording during rest condition from 20 young subjects and 20 mid-aged adults were studied. Partial Directed Coherence was computed by means of General Linear Kalman Filter and graph indexes were extracted from estimated patterns. At last small-worldness was used as feature for the SVM classifier. Results show that topological differences of brain networks exist between young and mid-aged adults: small-worldness is significantly different between the two populations and it can be used to classify the subjects with respect to age with an accuracy of 69%.