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-viewcontrastive 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, two extensions of HeCo are designed
to generate harder negative samples with high quality, which further boosts the
performance of HeCo. Extensive experiments conducted on a variety of real-world
networks show the superior performance of the proposed methods over the
state-of-the-arts.

本文研究了自监督的异构图神经网络，并提出了一种名为 HeCo 的新型协同对比学习机制，用于异构信息网络的节点嵌入学习，该方法利用两个视图提取两种嵌入，确保两个视图之间相互监督，学习高级节点嵌入，并使用两种 HeCo 扩展生成更难的高质量负样本，提高了性能。