Diffusion tensor imaging allows to infer brain connectivity from white matter, which can then be investigated aiming at finding possible biomarkers of disease. The usual initial step in graph construction is to identify the nodes in the brain using a predefined atlas. However, atlases are usually not considering the white matter structure. As a result, atlas-based brain parcellation and, hence, brain graphs are not fully considering the white matter organization. In this work, we are proposing an atlas-free scheme to map the structural brain networks. The idea is to identify the nodes in the brain exploiting the white matter structure inferred from the data. We first retrieve the white matter pathways from DTI, grouping fiber tracts into bundles. We then use these pathways in a clustering pipeline to identify the brain regions to map into the graph nodes, which are used to define the brain connectivity. We empirically tested the goodness of the proposed approach on a known case-control study obtaining results confirming findings in related literature.