HGCVAE: Integrating Generative and Contrastive Learning for Heterogeneous Graph Learning

Generative self-supervised learning (SSL) has exhibited significant potential and garnered increasing interest in graph learning. In this study, we aim to explore the problem of generative SSL in the context of heterogeneous graph learning (HGL). The previous SSL approaches for heterogeneous graphs have primarily relied on contrastive learning, necessitating the design of complex views to capture heterogeneity. However, existing generative SSL methods have not fully leveraged the capabilities of generative models to address the challenges of HGL. In this paper, we present HGCVAE, a novel contrastive variational graph auto-encoder that liberates HGL from the burden of intricate heterogeneity capturing. Instead of focusing on complicated heterogeneity, HGCVAE harnesses the full potential of generative SSL. HGCVAE innovatively consolidates contrastive learning with generative SSL, introducing several key innovations. Firstly, we employ a progressive mechanism to generate high-quality hard negative samples for contrastive learning, utilizing the power of variational inference. Additionally, we present a dynamic mask strategy to ensure effective and stable learning. Moreover, we propose an enhanced scaled cosine error as the criterion for better attribute reconstruction. As an initial step in combining generative and contrastive SSL, HGCVAE achieves remarkable results compared to various state-of-the-art baselines, confirming its superiority.