Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

TL;DR

This paper introduces HeCo, a self-supervised heterogenous graph neural network leveraging co-contrastive learning with cross-view mechanisms to improve node embedding quality without labels.

Contribution

The paper proposes a novel co-contrastive learning framework for HGNNs that utilizes cross-view contrast and view masking to enhance self-supervised learning.

Findings

HeCo outperforms state-of-the-art methods on real-world networks.

The cross-view contrastive mechanism effectively captures local and high-order structures.

Extensions generating harder negatives further improve performance.

Abstract

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.