  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.
