Complex networks have shown to be promising mechanisms to represent several aspects of nature, since their topological and structural features help in the understanding of relations, properties and intrinsic characteristics of the data. In this context, we propose to build music networks in order to find community structures of music genres. Our main contributions are twofold: 1) Define a totally unsupervised approach for music genres discrimination; 2) Incorporate topological features in music data analysis. We compared different distance metrics and clustering algorithms. Each song is represented by a vector of conditional probabilities for the note values in its percussion track. Initial results indicate the effectiveness of the proposed methodology.