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:

Deep Graph Neural Networks （GNNs） aim to capture both local and global features in complex networks， thereby alleviating the bottleneck in information propagation in graph-structured data. However， current deep GNN models often face the problem of feature over-correlation. Therefore， a Multi-scale Decorrelation graph convolutional network （Multi-Deprop） model was proposed. The model includes two operations： feature propagation and feature transformation. In feature propagation operation， multi-scale de-correlation parameters were introduced to maintain high de-correlation in lower network layers and weak de-correlation in higher network layers， thereby adapting to the needs of different hierarchical feature processing. In feature transformation operation， orthogonal regularization and maximal informatization loss were introduced， and orthogonal regularization was used to maintain feature independence and maximal informatization was used to maximize mutual information between the input and representation， thereby reducing feature information redundancy. Finally， comparison experiments were conducted on seven node classification datasets among the proposed model and four benchmark models. Experimental results show that the Multi-Deprop model achieves better node classification accuracy in most cases of models with 2 to 32 layers. Particularly on Cora dataset， the Multi-Deprop model has the accuracy of models with 4 to 32 layers improved by 0.80% to 13.28% compared to the benchmark model Deprop， which means the performance degradation problem in deep networks is solved by the proposed model in certain degree. In feature matrix correlation analysis， the feature matrix obtained using the Multi-Deprop model on Cora dataset has a correlation of 0.40， indicating weak correlation， demonstrating that the Multi-Deprop model alleviates the over-correlation issue significantly. The results of ablation studies and visualization experiments show that improvements in both operations contribute to enhancement of model performance. It can be seen that Multi-Deprop model reduces feature redundancy in deep networks significantly while ensuring high classification accuracy， and has strong generalization ability and practical value.

Key words: deep Graph Neural Network (GNN), over-correlation, L2 regularization, maximal informatization, multi-scale decorrelation