Fig. 5
Extraction of sequences from temporal network property data using a sliding window to use as input for machine learning. The vector Yi stores the targets to be learned for material i, i.e., encoding whether i is discovered by a given time-step t or not (as binary labels 1 and 0). Ci, ki, and \(\ell _i\) are examples for vectors of different network properties, encoding how those properties change over time as the network evolves, as explained in the text. The process of applying a sliding window (here with a width of w = 2) to extract sequences of features and targets (xi,t, yi,t) is illustrated. ML stands for the machine-learning task of training and testing classification algorithms using the extracted data
