Architectural distortion is a breast cancer sign, characterized by spiculated patterns that define the disease malignancy level. In this paper, the radial spiculae of a typical architectural distortion were characterized by a new strategy. Firstly, previously selected Regions of Interest are divided into a set of parallel and disjoint bands (4 pixels the ROI length), from which intensity integrals (coefficients) are calculated. This partition is rotated every two degrees, searching in the phase plane the characteristic radial spiculation. Then, these coefficients are used to construct a fully connected graph whose edges correspond to the integral values or coefficients and the nodes to x and y image positions. A centrality measure like the first eigenvector is used to extract a set of discriminant coefficients that represent the locations with higher linear energy. Finally, the approach is trained using a set of 24 Regions of Interest obtained from the MIAS database, namely, 12 Architectural Distortions and 12 controls. The first eigenvector is then used as input to a conventional Support Vector Machine classifier whose optimal parameters were obtained by a leave-one-out cross validation. The whole method was assessed in a set of 12 RoIs with different distribution of breast tissues (normal and abnormal), and the classification results were compared against a ground truth, already provided by the data base, showing a precision rate of 0.583%, a sensitivity rate of 0.833% and a specificity rate of 0.333%.