Self-supervised heterogeneous graph neural networks have shown remarkable effectiveness in addressing the challenge of limited labeled data. However, current contrastive learning methods face limitations in leveraging neighborhood information for each node. Some approaches utilize the local information of the target node, ignoring useful signals from deeper neighborhoods. On the other hand, simply stacking convolutional layers to expand the neighborhood inevitably leads to over-smoothing. To address the problems, we propose HGNN-DB, a Self-supervised Heterogeneous Graph Neural Network Based on Deep and Broad Neighborhood Encoding to tackle the over-smoothing problem within heterogeneous graphs. Specifically, HGNN-DB aims to learn informative node representations by incorporating both deep and broad neighborhoods. We introduce a deep neighborhood encoder with a distance-weighted strategy to capture deep features of target nodes. Additionally, a single-layer graph convolutional network is employed for the broad neighborhood encoder to aggregate broad features of target nodes. Furthermore, we introduce a collaborative contrastive mechanism to learn the complementarity and potential invariance between the two views of neighborhood information. Experimental results on four real-world datasets and seven baselines demonstrate that our model significantly outperforms the current state-of-the-art techniques on multiple downstream tasks. The codes and datasets for this work are available at https://github.com/SSQiana/HGNN-DB.
