A comprehensive survey on graph neural networks

Last year we looked at'Relational inductive biases, deep learning, and graph networks,' where the authors made the case for deep learning with structured representations, which are naturally represented as graphs. Today's paper choice provides us with a broad sweep of the graph neural network landscape. It's a survey paper, so you'll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them. We'll be talking about graphs as defined by a tuple where is the set of nodes (vertices), is the set of edges, and A is the adjacency matrix. An edge is a pair, and the adjacency matrix is an (for N nodes) matrix where if nodes and are not directly connected by a edge, and some weight value 0 if they are.