Heterogeneous graphs can well describe the complex entity relationships in
the real world. For example, online shopping networks contain multiple physical
types of consumers and products, as well as multiple relationship types such as
purchasing and favoriting. More and more scholars pay attention to this
research because heterogeneous graph representation learning shows strong
application potential in real-world scenarios. However, the existing
heterogeneous graph models use data augmentation techniques to enhance the use
of graph structure information, which only captures the graph structure
information from the spatial topology, ignoring the information displayed in
the spectrum dimension of the graph structure. To address the issue that
heterogeneous graph representation learning methods fail to model spectral
information, this paper introduces a spectral-enhanced graph contrastive
learning model (SHCL) and proposes a spectral augmentation algorithm for the
first time in heterogeneous graph neural networks. The proposed model learns an
adaptive topology augmentation scheme through the heterogeneous graph itself,
disrupting the structural information of the heterogeneous graph in the
spectrum dimension, and ultimately improving the learning effect of the model.
Experimental results on multiple real-world datasets demonstrate substantial
advantages of the proposed model.

本文介绍了一种光谱增强图对比学习模型（SHCL），首次在异构图神经网络中引入了一种光谱增强算法，通过异构图本身学习了一种自适应拓扑增强方案，扰乱了异构图的光谱维度的结构信息，从而最终提高了模型的学习效果。实验结果表明，该模型在多个真实数据集上具有显著优势。