A Practical Tutorial on Graph Neural Networks

• Main Authors: Ward, Isaac Ronald, Joyner, Jack, Lickfold, Casey, Guo, Yulan, Bennamoun, Mohammed
• Format: Journal Article
• Language: English
• Published: 11-10-2020
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Learning; Computer Science - Social and Information Networks
• DOI: 10.48550/arxiv.2010.05234

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.