A Practical Guide to Graph Neural Networks

NN variants have been designed to increase performance in certain problem domains; the convolutional neural network (CNN) excels in the context of image-based tasks, and the recurrent neural network (RNN) in the space of natural language processing and time series analysis. NNs have also been leveraged as components in composite DL frameworks -- they have been used as trainable generators and discriminators in generative adversarial networks (GANs), and as encoders and decoders in transformers [46]. Although they seem unrelated, the images used as inputs in computer vision, and the sentences used as inputs in natural language processing can both be represented by a single, general data structure: the graph (see Figure 1). Formally, a graph is a set of distinct vertices (representing items or entities) that are joined optionally to each other by edges (representing relationships). The learning architecture that has been designed to process said graphs is the titular graph neural network (GNN). Uniquely, the graphs fed into a GNN (during training and evaluation) do not have strict structural requirements per se; the number of vertices and edges between input graphs can change. In this way, GNNs can handle unstructured, non-Euclidean data [4], a property which makes them valuable in certain problem domains where graph data is abundant. Conversely, NN-based algorithms are typically required to operate on structured inputs with strictly defined dimensions.