Heterogeneous graph contrastive learning with spectral augmentation and dual aggregation

Abstract

Heterogeneous graphs effectively model complex entity relationships in real-world scenarios. However, existing methods primarily focus on topological structures, overlooking the spectral domain, which limits their ability to capture rich, multi-dimensional graph information. Many rely on meta-path schemes to encode semantic details of specific node types, neglecting others and local structural nuances. Thus, they fail to capture comprehensive structural information. To address these issues, a novel combined dual aggregation and spectral augmented algorithm, the heterogeneous graph contrast learning model (DasaHGCL), is proposed. It applies adaptive spectral augmentation introduced from homogeneous graph learning to the meta-path view of heterogeneous graphs, capturing their spectral invariance for the first time. It also creates an intra-scheme contrast mechanism in dual aggregation algorithms for meta-path and network schema, which circumvents the effect of differences between different aggregation schemes on the model to effectively capture higher-order semantic information and local heterogeneous structural features. Experiments on multiple real-world datasets demonstrate the clear advantages of DasaHGCL.