A Survey on Graph Classification and Link Prediction based on GNN
It provides a comprehensive overview for researchers and practitioners working with graph data, but it is incremental as it synthesizes existing methods without introducing new techniques.
This survey addresses the challenge of applying convolutional neural networks to non-Euclidean graph data, such as social and transportation networks, by reviewing graph convolutional and pooling operators, and summarizing models and applications in node classification, graph classification, and link prediction.
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.