Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and reference networks. The pivotal step in transferring convolutional neural networks to graph data analysis and processing lies in the construction of graph convolutional operators and graph pooling operators. This comprehensive review article delves into the world of graph convolutional neural networks. Firstly, it elaborates on the fundamentals of graph convolutional neural networks. Subsequently, it elucidates the graph neural network models based on attention mechanisms and autoencoders, summarizing their application in node classification, graph classification, and link prediction along with the associated datasets.

翻译：传统卷积神经网络局限于处理欧几里得空间数据，忽视了以图数据形式呈现的广阔现实场景，包括交通网络、社交网络和引用网络。将卷积神经网络迁移至图数据分析与处理的关键步骤在于图卷积算子和图池化算子的构建。本综述文章深入探讨了图卷积神经网络领域。首先，阐述了图卷积神经网络的基本原理。随后，系统阐释了基于注意力机制和自编码器的图神经网络模型，总结了其在节点分类、图分类和链接预测中的应用及所涉及的数据集。