FIGURE 5.

Complex Networks and data analytics. (A) Network analysis pipeline for orthodontics data. Once cephalometric variables are standardized to Z‐values, they are entered in a cross‐correlation process that returns a symmetric matrix, whose entries are the intervariable Pearson's correlation coefficients across subjects. A threshold is set to the matrix according to the P‐values associated with the coefficients. The final matrix (a weighted adjacency matrix) is translated into a network whose nodes are the cephalometric variables and the weights of the links the Pearson's correlation coefficients that survived the thresholding process. Finally, different metrics have been calculated from the network topology: centrality measures, modules or communities and the core‐periphery structure. (B) GNN. Low‐dimensional node representations are first learned from networks by graph embedding and then used as features to build specific classifiers for different tasks