Spectral Heterogeneous Graph Convolutions via Positive Noncommutative
  Polynomials
Mingguo He Zhewei Wei Shikun Feng Zhengjie Huang Weibin Li Yu Sun Dianhai Yu
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
https://github.com/ivam-he/PSHGCN.