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.

翻译：异构图神经网络（HGNNs）作为一种新兴技术，已展现出处理异构信息网络（HIN）的卓越能力。然而，大多数HGNNs采用半监督学习范式，这在现实应用中因标签稀缺而显著限制了其广泛使用。近年来，对比学习作为一种自监督方法，成为最令人振奋的学习范式之一，并在无标签场景中展现出巨大潜力。本文研究自监督HGNNs问题，并提出一种面向HGNNs的新型共对比学习机制——HeCo。与传统仅关注正负样本对比的对比学习不同，HeCo采用跨视图对比机制。具体而言，我们提出HIN的两个视图（网络模式视图和元路径视图）来学习节点嵌入，从而同时捕获局部和高阶结构。随后，提出跨视图对比学习及视图掩码机制，能够从两个视图中提取正负嵌入，使两个视图相互协同监督，最终学习到高层节点嵌入。此外，为进一步提升HeCo性能，我们设计了两种生成高质量更难负样本的附加方法。除不变因子外，视图特有因子互补地提供不同节点间的多样结构信息，这些信息也应包含在最终嵌入中。因此，我们需要进一步独立探索每个视图，并提出改进模型HeCo++。具体而言，HeCo++进行分层对比学习，包括跨视图对比和视图内对比，旨在加强对各自结构的挖掘。