Abstract: Graph convolutional neural network algorithms play a crucial role in the processing of graph structured data.The mainstream mode of existing graph convolutional networks is based on weighted summation of node features using Laplacian matrices,with a greater emphasis on optimizing the convolutional aggregation method and model structure,while ignoring the prior information of the graph data itself.To fully explore the rich attributes and structural information hidden behind graph data,and effectively reduce the proportion of noise in graph data,a bidirectional feature-enhanced graph convolutional network algorithm is proposed.The algorithm enhances the topological and attributes space features of graph data through node degree and similarity calculations,and then the two enhanced graph feature representations are propagated simultaneously in both topological and attribute spaces.The attention mechanism is used to adaptively fuse the learned embeddings.In addition,to address the issue of over-smoothing in deep graph convolutional neural networks,a multi-input residual structure is proposed,which combines initial resi-dual and high-order neighborhood residual to achieve balanced extraction of initial and high-order neighborhood features in any convolutional layers.Experiments are conducted on three public datasets,and the results show that using the proposed network achieves better classification performance than existing networks.

Key words: Graph convolutional network, Graph attention network, Graph data augmentation, Feature extraction, Node classification