基于盖根堡多项式最佳平方近似的谱图网络

doi:10.6040/j.issn.1672-3961.0.2023.150

摘要/Abstract

摘要： 针对现有谱图神经网络模型在学习图节点特征矩阵信号频率分布方面存在的不足,采用盖根堡正交基改进,提出一种泛化能力强、适合真实世界数据的谱图神经网络模型,有效提高节点分类任务精度。分析不同真实世界数据集中图节点特征矩阵的信号频率分布,使用盖根堡正交基学习谱图滤波函数,提高模型的泛化能力。理论分析表明,该模型能够以最佳平方误差有效学习闭区间上的任意连续谱滤波函数。在13个数据集上进行试验的结果显示,基于盖根堡正交基的谱图神经网络模型在8个数据集上的性能均超越目前的先进模型,验证了模型的有效性。可扩展性试验表明,该模型适用于大规模图数据。

关键词: 盖根堡正交基, 谱图神经网络, 图节点特征矩阵, 信号频率分布, 滤波函数

中图分类号:

TP391

林振宇,邵蓥侠. 基于盖根堡多项式最佳平方近似的谱图网络[J]. 山东大学学报 (工学版), 2024, 54(5): 93-100.

LIN Zhenyu, SHAO Yingxia. Spectral graph networks based on best square approximation with Gegenbauer polynomials[J]. Journal of Shandong University(Engineering Science), 2024, 54(5): 93-100.

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