Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging and regulatory networks in genetics. For this reason, Graph Neural Networks have recently emerged as an interesting methodology of analysing graphs whilst leveraging the power of deep learning. Being their ability to fit many real-world datasets, which have an inherent graph structure, GNNs have found applications in many different domains including: Applications of GNNs in computer vision include scene graph generation, point cloud classification and segmentation, action recognition, etc. Recognising semantic relationships between objects facilitates the understanding of the meaning of a visual scene. Scene graph generation models aim to parse an image into a semantic graph which consists of objects and their semantic relationships. Another application reverses the process by generating realistic images given scene graphs. This hints at the intriguing possibility of synthesising images given textual descriptions.