  Heterogeneous graph contrastive learning has received wide attention
recently. Some existing methods use meta-paths, which are sequences of object
types that capture semantic relationships between objects, to construct
contrastive views. However, most of them ignore the rich meta-path context
information that describes how two objects are connected by meta-paths.
Further, they fail to distinguish negative samples, which could adversely
affect the model performance. To address the problems, we propose MEOW, which
considers both meta-path contexts and weighted negative samples. Specifically,
MEOW constructs a coarse view and a fine-grained view for contrast. The former
reflects which objects are connected by meta-paths, while the latter uses
meta-path contexts and characterizes details on how the objects are connected.
Then, we theoretically analyze the InfoNCE loss and recognize its limitations
for computing gradients of negative samples. To better distinguish negative
samples, we learn hard-valued weights for them based on node clustering and use
prototypical contrastive learning to pull close embeddings of nodes in the same
cluster. In addition, we propose a variant model AdaMEOW that adaptively learns
soft-valued weights of negative samples to further improve node representation.
Finally, we conduct extensive experiments to show the superiority of MEOW and
AdaMEOW against other state-of-the-art methods.
