Self-supervised learning of invariant causal representation in heterogeneous information network

Abstract

Invariant learning on graphs is essential for uncovering causal relationships in complex phenomena. However, most research has focused on homogeneous information networks with single node and edge types, ignoring the rich heterogeneity of real-world systems. Additionally, many invariant learning methods rely on labeled data and the design of complex graph augmentation or contrastive sampling algorithms, requiring domain-specific expertise or substantial human resources, making them difficult to implement in practical applications. To overcome these limitations, we propose a Generative-Contrastive Collaborative Self-Supervised Learning (GCCS) framework. This framework combines the ability of generative learning to mine supervisory signals from the data itself with the capacity of contrastive learning to learn invariant representations, enabling self-supervised learning of invariant causal representations from heterogeneous information networks (HINs). Specifically, generative self-supervised learning (SSL) constructs meta-path aware adjacency matrices and performs a mask-reconstruct operation, while contrastive SSL refines the learned representations by enforcing similarity and consensus constraints across different views. This joint optimization captures invariant causal features, enhancing the model’s robustness. Extensive experiments on three real-world HINs datasets demonstrate that GCCS outperforms state-of-the-art baselines, particularly in noisy and complex environments, showcasing its superior performance and robustness for self-supervised learning in heterogeneous graph structures.