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 discriminators for downstream tasks. However, data augmentation is still limited due to the discrete and abstract nature of graphs. To tackle the above limitations, we propose a novel \textit{Generative-Contrastive Heterogeneous Graph Neural Network (GC-HGNN)}. Specifically, we first propose a heterogeneous graph generative learning enhanced contrastive paradigm. This paradigm includes: 1) A contrastive view augmentation strategy by using masked autoencoder. 2) Position-aware and semantics-aware positive sample sampling strategy for generate 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 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://github.com/xxx.

翻译：异构图（HGs）通过多类型节点和边有效建模现实世界中的复杂关系。近年来，受自监督学习启发，基于数据增强和判别器的对比异构图神经网络（HGNNs）在下游任务中展现出巨大潜力。然而，由于图的离散性和抽象性，数据增强仍存在局限性。为解决上述问题，我们提出了一种新颖的**生成-对比异构图神经网络（GC-HGNN）**。具体而言，我们首先提出了一种异构图生成学习增强的对比范式，该范式包括：1）基于掩码自编码器的对比视图增强策略；2）面向位置感知和语义感知的正样本采样策略，用于生成困难负样本；3）用于捕获局部与全局信息的分层对比学习策略。此外，分层对比学习与采样策略旨在从生成-对比视角构建增强型判别器。最终，我们在八个真实数据集上将模型与十七个基线方法进行对比。在节点分类和链路预测任务中，我们的模型优于最新的对比与生成基线方法。为复现研究，我们已将代码开源至https://github.com/xxx。