  Inspired by the success of contrastive learning (CL) in computer vision and
natural language processing, graph contrastive learning (GCL) has been
developed to learn discriminative node representations on graph datasets.
However, the development of GCL on Heterogeneous Information Networks (HINs) is
still in the infant stage. For example, it is unclear how to augment the HINs
without substantially altering the underlying semantics, and how to design the
contrastive objective to fully capture the rich semantics. Moreover, early
investigations demonstrate that CL suffers from sampling bias, whereas
conventional debiasing techniques are empirically shown to be inadequate for
GCL. How to mitigate the sampling bias for heterogeneous GCL is another
important problem. To address the aforementioned challenges, we propose a novel
Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In
particular, we use metapaths as the augmentation to generate multiple subgraphs
as multi-views, and propose a contrastive objective to maximize the mutual
information between any pairs of metapath-induced views. To alleviate the
sampling bias, we further propose a positive sampling strategy to explicitly
select positives for each node via jointly considering semantic and structural
information preserved on each metapath view. Extensive experiments demonstrate
HGCML consistently outperforms state-of-the-art baselines on five real-world
benchmark datasets.
