F. Cen, S. Liu, Q. Feng, T. He, J. Xu, "HPoolGCL: Augmentation-Free Cross-Granularity Graph Contrastive Learning With Hierarchical Pooling," CAAI Transactions on Intelligence Technology, 2026.
Abstract:
Graph contrastive learning (GCL) has emerged as a dominant paradigm for self-supervised representation learning for attributed graph data. However, existing GCL methods heavily rely on empirical graph data augmentation, which may distort intrinsic graph semantics and produce poor generalisation without carefully chosen or designed augmentation techniques. Furthermore, most GCL approaches focus on same-granularity contrastive learning (e.g., node vs. node), neglecting the hierarchical and multigranular properties inherent in real-world networks, leading to suboptimal performance. To address these limitations, we propose HPoolGCL, a cross-granularity GCL framework compatible with various hierarchical graph pooling methods to capture multigranularity information. Our framework eliminates the need for handcrafted augmentations, explicit negative sampling and complex multiencoder architectures by applying two novel loss functions in hierarchical graph pooling. The theoretical analysis is provided to explain the effectiveness of unified MGC and HiCR losses from three perspectives, namely, the information maximisation principle, the redundancy reduction principle and the information bottleneck principle. The experimental results demonstrate that HPoolGCL achieves state-of-the-art performance across multiple downstream tasks on five benchmarks. Our codes are available at https://github.com/Heycen/HPoolGCL.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
This is the peer reviewed version of the following article: F. Cen, S. Liu, Q. Feng, T. He, J. Xu, "HPoolGCL: Augmentation-Free Cross-Granularity Graph Contrastive Learning With Hierarchical Pooling," CAAI Transactions on Intelligence Technology, 2026., which has been published in final form at https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70096. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.