Graph Neural Networks: Taxonomy, Advances and Trends
This survey provides a structured overview and future directions for researchers and practitioners working with graph neural networks, helping them navigate the vast and rapidly evolving landscape of GNNs.
This paper presents a comprehensive survey of graph neural networks (GNNs), proposing a novel taxonomy and reviewing approximately 400 relevant literatures. It aims to provide a panoramic view of GNNs, classifying existing works into defined categories.
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.