Aggregation Enhanced Graph Convolutional Network for Graph Classification

Limited by the irregularity of graph topological structure and the sequence independence of nodes, existing graph neural networks usually generate graph-level representation by simple aggregation or sorting of node features for graph classification. These models are usually not deep enough to extract more abstract semantic information. Once the network deepens, it is easy to cause over-smoothing. To solve this problem, we propose a simple and efficient graph convolutional neural network based on DenseNet called AEGCN (Aggregation Enhanced Graph Convolutional Network) for graph clas-sification. We build a local extrema function named ELEConv (Enhanced Local Extrema Convolution) to reduce the noise in graphs, and then generate a large number of reusable feature maps through dense links. Extensive experiments on four real-world datasets validate that AEGCN not only alleviates the over-smoothing problem, but also has an advanced graph classification effect.