Related papers: A Practical Tutorial on Graph Neural Networks

A Practical Tutorial on Graph Neural Networks

URL: http://arxiv.org/abs/2010.05234v3
Date: Sat, 25 Dec 2021 09:06:24 GMT
Title: A Practical Tutorial on Graph Neural Networks
Authors: Isaac Ronald Ward, Jack Joyner, Casey Lickfold, Yulan Guo and Mohammed Bennamoun
Abstract summary: Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) This tutorial exposes the power and novelty of GNNs to AI practitioners.
Score: 49.919443059032226
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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