  Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto
model for analyzing HGs, while most of them rely on a relative large number of
labeled data. In this work, we investigate Contrastive Learning (CL), a key
component in self-supervised approaches, on HGs to alleviate the label scarcity
problem. We first generate multiple semantic views according to metapaths and
network schemas. Then, by pushing node embeddings corresponding to different
semantic views close to each other (positives) and pulling other embeddings
apart (negatives), one can obtain informative representations without human
annotations. However, this CL approach ignores the relative hardness of
negative samples, which may lead to suboptimal performance. Considering the
complex graph structure and the smoothing nature of GNNs, we propose a
structure-aware hard negative mining scheme that measures hardness by
structural characteristics for HGs. By synthesizing more negative nodes, we
give larger weights to harder negatives with limited computational overhead to
further boost the performance. Empirical studies on three real-world datasets
show the effectiveness of our proposed method. The proposed method consistently
outperforms existing state-of-the-art methods and notably, even surpasses
several supervised counterparts.
