A survey on graph neural networks

Abstract

In recent years, we have witnessed the developments that deep learning has brought to machine learning. It has solved many problems in the areas of computer vision, speech recognition, natural language processing, and various other tasks with state of-the-art performance. However, the data in these tasks is typically represented in Euclidean space. As technology develops, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. This is because, due to the uniqueness of graphs, applying deep learning to the ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, Graph Neural Networks (GNNs) have emerged. A Graph Neural Network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. This paper introduces commonly used graph neural networks, their learning methods, and common datasets for graph neural networks. It also provides an outlook on the future of Graph Neural Networks.