Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning (2105.09111v1)

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.

• The paper introduces HeCo, a co-contrastive learning framework that leverages dual views—network schema and meta-path—to enhance node embeddings.
• It employs innovative negative sampling techniques, including GAN-based and MixUp strategies, to generate challenging contrastive tasks.
• Empirical results demonstrate that HeCo outperforms existing methods in node classification and clustering on datasets like ACM and DBLP.

An Analysis of "Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning"

The paper "Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning" by Xiao Wang et al., presents an exploration into the burgeoning field of heterogeneous information networks (HINs) utilizing graph neural networks (GNNs). The paper proposes a novel architecture named HeCo, integrating self-supervised learning and a co-contrastive approach to efficiently process HINs without relying on labeled data, which are often scarce or labor-intensive to obtain in practical applications.

Core Contributions

1. Co-contrastive Learning Mechanism: The paper introduces a co-contrastive learning framework for heterogeneous graph neural networks that employs cross-view contrastive mechanisms. Unlike conventional methods that contrast positive and negative samples derived from the same view, HeCo extends this paradigm by contrasting node embeddings derived from two separate views: network schema and meta-path. This dual-view system enhances the embeddings' capture of both local and high-order network structures.
2. Heterogeneous Views Utilization: HeCo utilizes two specific views—network schema and meta-path—to encapsulate distinct structural information. Network schema view captures local neighborhood structures, whereas meta-path view focuses on high-order semantics via paths that connect node types. This dichotomy allows HeCo to craft richer node representations.
3. Innovative Negative Sampling: The paper strategically designs tasks that include generating high-quality negative samples to increase the efficacy of HeCo. The authors developed two extensions, HeCo_GAN and HeCo_MU, which enhance the difficulty of contrastive tasks by producing more challenging negative samples. HeCo_GAN utilizes GAN-based techniques to generate adversarial examples, while HeCo_MU employs a MixUp strategy to synthesize harder negatives.

Performance Evaluation

The empirical assessment of HeCo, conducted on various real-world networks, demonstrates its superiority over existing methods. Specifically, HeCo consistently exhibited improved performance in node classification and clustering tasks across datasets such as ACM and DBLP, often outperforming semi-supervised methods. These results underscore the practical potential of self-supervised approaches in scenarios where labeled data are limited.

Implications and Future Directions

HeCo's deployment in HINs opens numerous avenues for future research. Its ability to utilize self-supervised learning can be particularly beneficial in domains requiring extensive and diverse datasets, such as biomedical networks and social networks, where manual labeling is arduous. Furthermore, the paper suggests the applicability of HeCo's co-contrastive learning framework to other complex network structures beyond HINs, potentially fostering more generalized node embeddings for diverse graph analytics tasks.

HeCo's strategy of co-contrastive learning could stimulate the development of more advanced heterogeneous graph neural networks that can seamlessly integrate multi-view information. Future work could explore adaptive mechanisms to dynamically adjust view-specific biases based on the dataset characteristics, improving HeCo's flexibility and capacity to handle even more intricate heterogeneity in graph data.

In summary, the presentation of HeCo in this paper provides a decisive step toward more efficient self-supervised learning methodologies in the field of heterogeneous graph neural networks, offering compelling advancements in learning informative node embeddings without the dependency on large sets of labeled data.