This paper aims to provide a novel design of a multiscale framelets convolution for spectral graph neural networks. In the spectral paradigm, spectral GNNs improve graph learning task performance via proposing various spectral filters in spectral domain to capture both global and local graph structure information. Although the existing spectral approaches show superior performance in some graphs, they suffer from lack of flexibility and being fragile when graph information are incomplete or perturbated. Our new framelets convolution incorporates the filtering func-tions directly designed in the spectral domain to overcome these limitations. The proposed convolution shows a great flexibility in cutting-off spectral information and effectively mitigate the negative effect of noisy graph signals. Besides, to exploit the heterogeneity in real-world graph data, the heterogeneous graph neural network with our new framelet convolution provides a solution for embedding the intrinsic topological information of meta-path with a multi-level graph analysis.Extensive experiments have been conducted on real-world heterogeneous graphs and homogeneous graphs under settings with noisy node features and superior performance results are achieved.

本文提出了一种新颖的多尺度框架卷积设计用于谱图神经网络，该方法集成了直接设计在谱域中的过滤函数以提高对嘈杂图信号的鲁棒性，有效地减轻了嘈杂信号的负面影响，并利用异构图神经网络和多级图分析，嵌入了元路径上的拓扑信息。在现实世界的异构图和同构图中的实验表明，该方法取得了优越的性能。

拟框架波：图神经网络的另一项改进