A Practical Tutorial on Graph Neural Networks

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 neur...

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Bibliographic Details
Published in:arXiv.org 2021-12
Main Authors: Ward, Isaac Ronald, Joyner, Jack, Casey Lickfold, Guo, Yulan, Bennamoun, Mohammed
Format: Article
Language:English
Subjects:
Artificial intelligence
Collating
Deep learning
Graph neural networks
Neural networks
Unstructured data
Online Access:Get full text
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Summary: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.
ISSN:2331-8422