Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible. Some of the interesting and useful applications on these graphs are graph classification, node classification, link prediction, etc. The Graph Neural Networks have evolved over the last few years. Graph Neural Networks (GNNs) are efficient ways to get insight into large and dynamic graph datasets capturing relationships among billions of entities also known as knowledge graphs. In this paper, we discuss the graph convolutional neural networks graph autoencoders and spatio-temporal graph neural networks. The representations of the graph in lower dimensions can be learned using these methods. The representations in lower dimensions can be used further for downstream machine learning tasks.

翻译：以节点和边缘形式绘制的图表显示的知识应尽可能保留原始数据的许多特征。这些图表上的一些有趣和有用的应用包括图表分类、节点分类、链接预测等。图表神经网络在过去几年中不断演变。图表神经网络(GNN)是深入了解大型和动态图表数据集的有效方法,这些数据集捕捉了数十亿实体(也称为知识图)之间的关系。在本文件中,我们讨论了图表进化神经网络图解自动电解器和时空图神经网络。可通过这些方法了解下层图的图解。低层图解可用于下游机器学习任务。