After CNNs exploded in 2012, showing unprecedented levels of prediction accuracy on image classification tasks, a group of researchers from Yann LeCun’s team decided to extend their success to other, more exotic domains. Specifically, they started working on generalizing convnets to graphs. Their efforts were described in this influential paper. 

Since then, Graph Neural Networks have become a hot area of research within the ML community and beyond. Numerous papers have been published explaining how different kinds and flavors of GNNs can be applied to complicated, irregular, high-dimensional, non-grid-like structures (graphs, manifolds, meshes, etc.).