From Graph ML to Deep Relational Learning

Graph structured data are all around us. With the recent advent of deep learning, it seems only natural that researchers started to explore this data representation with neural networks, too. Currently, we experience an explosion of the Graph Neural Network (GNN) class, with countless models being proposed under a variety of (catchy) names. Nevertheless, most of these models are based on the same simple graph propagation principle. To look at the problem from a broader view, we will here reveal the underlying GNN principles from the general perspective of Relational Machine Learning, which we discussed in a previous article.