关键词: 深度图神经网络, 过度相关, L2正则化, 最大信息化, 多尺度去相关

Abstract: Abstract: In view of the performance degradation of depth graph neural networks and excessive correlation of feature matrices, a multi-scale de-correlation graph convolutional network (Multi-Deprop) was proposed. Firstly, in the feature propagation operation, multi-scale de-correlation parameters are used to make the network maintain high de-correlation of low-level network and weak de-correlation of high-level network in the propagation process, so as to adapt to the needs of feature processing at different levels. Secondly, in the feature transformation operation, orthogonal regularization and maximum information loss are introduced to help alleviate over-correlation problems, and the orthogonal regularization loss maintains feature independence. Maximum informationization maximizes the mutual information between inputs and representations to reduce the redundancy of feature information. Finally, the comparison experiment with four benchmark models on the seven node classification data set shows that Multi-Deprop can achieve better accuracy of node classification in 2-32 layers, especially in high layer numbers, which alleviates the performance degradation of deep network. For the correlation analysis of the feature matrix, the Multi-Deprop correlation in the Cora dataset is about 0.4, which greatly alleviates the over-correlation problem. Ablation experiments also show that the improvement of the two modules can improve the performance of the model to some extent.

Key words: deep graph neural network, over-correlation, L2 regularization, maximum informatization, Multi-scale decorrelation