  Heterogeneous graph neural networks (HGNNs) as an emerging technique have
shown superior capacity of dealing with heterogeneous information network
(HIN). However, most HGNNs follow a semi-supervised learning manner, which
notably limits their wide use in reality since labels are usually scarce in
real applications. Recently, contrastive learning, a self-supervised method,
becomes one of the most exciting learning paradigms and shows great potential
when there are no labels. In this paper, we study the problem of
self-supervised HGNNs and propose a novel co-contrastive learning mechanism for
HGNNs, named HeCo. Different from traditional contrastive learning which only
focuses on contrasting positive and negative samples, HeCo employs cross-view
contrastive mechanism. Specifically, two views of a HIN (network schema and
meta-path views) are proposed to learn node embeddings, so as to capture both
of local and high-order structures simultaneously. Then the cross-view
contrastive learning, as well as a view mask mechanism, is proposed, which is
able to extract the positive and negative embeddings from two views. This
enables the two views to collaboratively supervise each other and finally learn
high-level node embeddings. Moreover, to further boost the performance of HeCo,
two additional methods are designed to generate harder negative samples with
high quality. Besides the invariant factors, view-specific factors
complementally provide the diverse structure information between different
nodes, which also should be contained into the final embeddings. Therefore, we
need to further explore each view independently and propose a modified model,
called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning,
including cross-view and intra-view contrasts, which aims to enhance the mining
of respective structures.

Machine Learning