  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.
