论文标题
图神经网络：分类学，进步和趋势
Graph Neural Networks: Taxonomy, Advances and Trends
论文作者
论文摘要
图形神经网络提供了一个强大的工具包，可根据特定任务将现实世界图嵌入到低维空间中。到目前为止，有关此主题的几项调查。但是，它们通常强调不同的角度，因此读者看不到图形神经网络的全景。这项调查旨在克服这一限制，并就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了一种新颖的分类法，然后参考了最多400个相关文献，以显示图神经网络的全景。所有这些都分为相应的类别。为了将图形神经网络推向新阶段，我们总结了四个未来的研究方向，以克服面临的挑战。可以预期，越来越多的学者可以理解和利用图形神经网络，并在其研究社区中使用它们。
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.
