  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.
