Hypergraph Self-supervised Learning with Sampling-efficient Signals
<|reference_start|>Hypergraph Self-supervised Learning with Sampling-efficient Signals: Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discrimination strategy, suffering from two significant limitations: (1) They select negative samples arbitrarily, which is unreliable in deciding similar and dissimilar pairs, causing training bias. (2) They often require a large number of negative samples, resulting in expensive computational costs. To address the above issues, we propose SE-HSSL, a hypergraph SSL framework with three sampling-efficient self-supervised signals. Specifically, we introduce two sampling-free objectives leveraging the canonical correlation analysis as the node-level and group-level self-supervised signals. Additionally, we develop a novel hierarchical membership-level contrast objective motivated by the cascading overlap relationship in hypergraphs, which can further reduce membership sampling bias and improve the efficiency of sample utilization. Through comprehensive experiments on 7 real-world hypergraphs, we demonstrate the superiority of our approach over the state-of-the-art method in terms of both effectiveness and efficiency.<|reference_end|>
@article{li2024hypergraph,
    title={Hypergraph Self-supervised Learning with Sampling-efficient Signals},
    author={Fan Li, Xiaoyang Wang, Dawei Cheng, Wenjie Zhang, Ying Zhang, and
  Xuemin Lin},
    journal={arXiv preprint arXiv:2404.11825},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11825},
    primaryClass={cs.LG}
}