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.

SE-HSSL 是一个基于超图的自我监督学习框架，通过两个无需采样的目标和一个基于层次关系的对比目标来解决超图自我监督学习模型中的负样本采样偏差和计算效率问题。实验证明，与现有方法相比，SE-HSSL 在有效性和效率上都具有优势。