Spectral graph networks based on best square approximation with Gegenbauer polynomials
To address the limitations of existing spectral graph neural network models in learning the frequency distribution of signals in graph node feature matrices,a Gegenbauer-based spectral graph neural network model with strong generalization ability was pro-posed,suitable for real-world data,which effectively improved node classification accuracy.The signal frequency distribution in graph node feature matrices from various real-world datasets was analyzed,and a method using the Gegenbauer orthogonal basis to learn spectral graph filtering functions was proposed,enhancing the model's generalization ability.Theoretical analysis demonstrated that the model was capable of effectively learning arbitrary continuous spectral filtering functions on closed intervals with the best square error.Experiments conducted on 13 datasets showed that the performance of the Gegenbauer-based spectral graph neural net-work model surpassed advanced models on 8 out of 13 datasets,which confirmed the model's effectiveness.Scalability experiments indicated that the model was applicable to large-scale graph data.
Gegenbauer orthogonal basisspectral graph neural networkgraph node feature matrixsignal frequency distributionfiltering function
万方数据
维普
