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.
翻译：图形神经网络为根据具体任务将真实世界图形嵌入低维空间提供了一个强大的工具包。 到目前为止,已经就此专题进行了几次调查。 但是,它们通常强调不同角度,使读者看不到图形神经网络的全景。 此次调查旨在克服这一局限性,并对图形神经网络进行全面审查。 首先,我们为图形神经网络提供一个新的分类,然后引用多达400种相关文献,以显示图形神经网络的全貌。 所有这些文献都被分类为相应的类别。 为了将图形神经网络推入一个新阶段,我们总结了四个未来研究方向,以克服面临的挑战。 预计更多学者能够理解和利用图形神经网络,并在研究界使用这些网络。