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表征学习中展现出显著潜力并日益受到关注。本研究旨在探索生成式自监督学习在异构图学习问题中的适用性。现有异构图自监督方法主要依赖对比学习，需设计复杂视图以捕获异质性，但既有生成式自监督方法未能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，该模型摆脱了复杂异质性捕获的负担，转而充分发挥生成式自监督学习的潜力。HGCVAE通过三大核心创新将对比学习与生成式自监督学习有机结合：首先，利用变分推理能力设计渐进式机制生成高质量困难负样本以增强对比学习；其次，提出动态掩码策略保障学习的有效性与稳定性；最后，引入增强型缩放余弦误差作为属性重构的优化准则。作为生成式与对比式自监督学习的融合先例，HGCVAE在多种先进基线方法中取得显著优势，验证了其优越性能。