CCAHCL: Multi-Level Hypergraph Contrastive Learning for Connected Component Awareness
Zhuo Li, Gengyu Lyu, Yuena Lin, Ziang Chen, Zhiyuan Ma, Zhen Yang, Zun Li
Abstract
Hypergraph contrastive learning has emerged as a powerful unsupervised paradigm for hypergraph representation learning. Traditional hypergraph contrastive learning methods typically leverage neighbor aggregation strategy to obtain entity (node and hyperedge) representations within each connected component, and then utilize contrastive losses (e.g., node- or hyperedge-level) to update the encoders. However, since entities are usually focused equally on their respective losses, large connected components with numerous entities tend to provide a dominant contribution to the whole learning process, which inevitably hinders the effective learning of entity representations within small connected components. To address this issue, we propose a novel Connected-Component-Aware Hypergraph Contrastive Learning method (CCAHCL). Different from previous methods that only construct node or hyperedge representations, our method additionally constructs the connected component representations, and accordingly designs a hierarchical contrastive loss to balance the model
BibTeX
@inproceedings{aaai2026_ccahclmultilevel,
title = {CCAHCL: Multi-Level Hypergraph Contrastive Learning for Connected Component Awareness},
author = {Zhuo Li and Gengyu Lyu and Yuena Lin and Ziang Chen and Zhiyuan Ma and Zhen Yang and Zun Li},
booktitle = {AAAI 2026},
year = {2026}
}