Heterogeneous Graphs (HGs) can effectively model complex relationships in the real world by multi-type nodes and edges. In recent years, inspired by self-supervised learning, contrastive Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential by utilizing data augmentation and contrastive discriminators for downstream tasks. However, data augmentation is still limited due to the graph data's integrity. Furthermore, the contrastive discriminators remain sampling bias and lack local heterogeneous information. To tackle the above limitations, we propose a novel Generative-Enhanced Heterogeneous Graph Contrastive Learning (GHGCL). Specifically, we first propose a heterogeneous graph generative learning enhanced contrastive paradigm. This paradigm includes: 1) A contrastive view augmentation strategy by using a masked autoencoder. 2) Position-aware and semantics-aware positive sample sampling strategy for generating hard negative samples. 3) A hierarchical contrastive learning strategy for capturing local and global information. Furthermore, the hierarchical contrastive learning and sampling strategies aim to constitute an enhanced contrastive discriminator under the generative-contrastive perspective. Finally, we compare our model with seventeen baselines on eight real-world datasets. Our model outperforms the latest contrastive and generative baselines on node classification and link prediction tasks. To reproduce our work, we have open-sourced our code at https://anonymous.4open.science/r/GC-HGNN-E50C.

翻译：异质图（HGs）通过多类型节点和边能够有效建模真实世界中的复杂关系。近年来，受自监督学习启发，对比异质图神经网络（HGNNs）通过利用数据增强和对比判别器在下游任务中展现出巨大潜力。然而，由于图数据完整性的约束，数据增强仍存在局限性。此外，对比判别器存在采样偏差问题，且缺乏局部异质性信息。为解决上述局限性，我们提出了一种新型的生成增强型异质图对比学习（GHGCL）方法。具体而言，我们首先提出了一种异质图生成学习增强的对比范式。该范式包含：1) 基于掩码自编码器的对比视图增强策略；2) 位置感知与语义感知的正样本采样策略，用于生成困难负样本；3) 分层对比学习策略，用于捕捉局部与全局信息。此外，分层对比学习与采样策略旨在生成-对比视角下构建增强型对比判别器。最后，我们在八个真实世界数据集上将模型与十七个基线方法进行对比。在节点分类和链接预测任务上，我们的模型超越了最新的对比学习与生成学习基线方法。为促进研究复现，我们已在 https://anonymous.4open.science/r/GC-HGNN-E50C 开源了代码。