Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They have been developed and are presented in this course as generalizations of the convolutional neural networks (CNNs) that are used to process signals in time and space. Depending on how much you have heard of neural networks (NNs) and deep learning, this is a sentence that may sound strange. Aren’t CNNs just particular cases of NN? And isn’t the same true of GNNs? In a strict sense they are, but our focus on this course is in large scale problems involving high dimensional signals. In these settings NNs fail to scale. CNNs are the tool for enabling scalable learning for signals in time and space. GNNS are the tool for enabling scalable learning for signals supported on graphs.