Graph Neural Networks: Taxonomy, Advances and Trends

TL;DR

This comprehensive survey offers a new taxonomy and overview of graph neural networks, covering 400 studies, and outlines future research directions to advance the field.

Contribution

It introduces a novel taxonomy for GNNs, classifies extensive literature, and proposes future research directions to overcome current challenges.

Findings

Provides a comprehensive taxonomy of GNNs

Classifies up to 400 relevant studies

Summarizes four future research directions

Abstract

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.