Humans represent complex systems as compositions of entities and interactions among these entities, i.e., a graph. In this session, we particularly focus on the algorithmic and theoretical foundations of Graph Neural Networks (GNNs). GNNs are a family of methods that generalize and extend neural networks to operate on relational data, and provides a flexible interface for manipulating structured knowledge, learning structured representations, and relational reasoning. Despite the rapid growth in the past five years, there are several limitations to applying and generalizing current GNN techniques to model datasets in novel applications, e.g., computer vision, graphics, wireless communication, etc.”