A Practical Tutorial on Graph Neural Networks

TL;DR

This tutorial provides an accessible overview of graph neural networks, explaining their motivations, concepts, mathematics, and applications with practical examples to help AI practitioners understand their power and novelty.

Contribution

It offers a concise, practical tutorial on GNNs, collating key concepts, mathematics, and applications for AI practitioners.

Findings

GNNs effectively handle unstructured data types.

Various GNN variants demonstrate strong performance.

Practical examples enhance understanding of GNN applications.

Abstract

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.