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.

翻译：生成式自监督学习在图学习中展现出巨大潜力并受到日益关注。本研究旨在探索异构图学习背景下生成式自监督学习的问题。现有异构图自监督学习方法主要依赖对比学习，需要设计复杂视图以捕捉异质性。然而，现有生成式自监督学习方法尚未充分发挥生成模型应对异构图学习挑战的能力。本文提出HGCVAE——一种新型对比变分图自编码器，将异构图学习从复杂异质性捕捉的负担中解放出来。HGCVAE不再聚焦于复杂的异质性，而是充分释放生成式自监督学习的潜力。该模型创新性地将对比学习与生成式自监督学习相结合，引入多项关键创新：首先，利用变分推断能力，采用渐进式机制为对比学习生成高质量困难负样本；其次，提出动态掩码策略保障学习的有效性与稳定性；此外，提出增强型缩放余弦误差作为更优的属性重建准则。作为融合生成式与对比自监督学习的初步探索，HGCVAE相较于各类先进基线方法取得了显著成果，验证了其优越性。