Abstract: GNNs are specialized models designed for graph data and have been successfully applied to various graph learning tasks such as node classification and link prediction.However,most existing GNN models are based on the message-passing paradigm,which fails to fully capture the multi-dimensional relationships between structural information and feature information of nodes.Additionally,traditional activation functions often lead to information loss and lack interpretability in the models.To address these challenges,this paper proposes a novel Kolmogorov-Arnold Network-based Dual-Channel Graph Neural Network(KDCGNN).KDCGNN employs structural convolution and feature convolution in two separate channels to extract structural and feature information from graphs,generating structural and feature encodings for nodes.These encodings are then fused through concatenation and further transformed using the Kolmogorov-Arnold Network to enhance classification performance and model interpretability.Furthermore,a consistency loss function is introduced to encourage distributional alignment between structural and feature encodings,thereby improving the generalization capability of the model.Experiments on three benchmark citation network datasets(Cora,Citeseer,and Pubmed) demonstrate that KDCGNN outperforms existing baseline methods in node classification tasks.KDCGNN provides a novel approach to improving the interpretability and performance of graph neural networks.

Key words: Graph neural networks, Kolmogorov-Arnold networks, Dual-channel mechanism, Node classification, Gaussian-Dice similarity