Work

A Practical Tutorial on Graph Neural Networks

Year: 2022

Type: article

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.

Source: ACM Computing Surveys

DOI: 10.1145/3503043

Authors: Isaac Ronald Ward, Jack Joyner, Casey Lickfold, Yulan Guo, Mohammed Bennamoun

Institutions: University of Southern California, Sun Yat-sen University, The University of Western Australia

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Australian Research Council