A Practical Tutorial on Graph Neural Networks
It serves as an incremental educational resource for AI practitioners seeking to learn about GNNs.
This tutorial addresses the need for accessible education on graph neural networks (GNNs) by collating and presenting details on their motivations, concepts, mathematics, and applications, providing a concise and practical resource for AI practitioners.
Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements represent a departure from traditional deep learning techniques. This tutorial exposes the power and novelty of GNNs to AI practitioners by collating and presenting details regarding the motivations, concepts, mathematics, and applications of the most common and performant variants of GNNs. Importantly, we present this tutorial concisely, alongside practical examples, thus providing a practical and accessible tutorial on the topic of GNNs.