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.

通过利用自监督学习，我们提出了一种新颖的生成对比异构图神经网络 (GC-HGNN)，并且在结点分类和链接预测任务中显著超越最新的对比和生成基线模型。