A Practical Tutorial on Graph Neural Networks

doi:10.1145/3503043

A Practical Tutorial on Graph Neural Networks

2022-09-13

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DOI：10.1145/3503043

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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.

Keywords:

Graph neural network

tutorial

artificial intelligence

recurrent

convolutional

auto encoder

decoder

machine learning

deep learning

papers with code

theory

applications

A Practical Tutorial on Graph Neural Networks

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