Heterogeneous Graph Neural Networks (HGNNs) have gained significant
popularity in various heterogeneous graph learning tasks. However, most HGNNs
rely on spatial domain-based message passing and attention modules for
information propagation and aggregation. These spatial-based HGNNs neglect the
utilization of spectral graph convolutions, which are the foundation of Graph
Convolutional Networks (GCN) on homogeneous graphs. Inspired by the
effectiveness and scalability of spectral-based GNNs on homogeneous graphs,
this paper explores the extension of spectral-based GNNs to heterogeneous
graphs. We propose PSHGCN, a novel heterogeneous convolutional network based on
positive noncommutative polynomials. PSHGCN provides a simple yet effective
approach for learning spectral graph convolutions on heterogeneous graphs.
Moreover, we demonstrate the rationale of PSHGCN in graph optimization. We
conducted an extensive experimental study to show that PSHGCN can learn diverse
spectral heterogeneous graph convolutions and achieve superior performance in
node classification tasks. Our code is available at
this https URL

本文提出了一种基于正非交换多项式的新颖异构卷积神经网络 ——PSHGCN，可用于在异构图中学习谱图卷积，实现节点分类任务中卓越的性能。