Figure 1

Architecture of MoGAN. The generator (a Convolutional Neural Network or CNN) performs transposed convolution operations that upsample the input random noise vector, transforming it into a \(64\times 64\) adjacency matrix representing a mobility network. The discriminator (a CNN) takes as input both the generated mobility networks and the real ones from the training set and performs a series of convolutional operations that end up with a probability, for each sample, to be fake or real. Both the discriminator’s and generator’s weights are then backpropagated