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Fairness-Aware Hypergraph Self-Supervised Learning With Sampling-Efficient Signals
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DOI：10.1109/TKDE.2026.3676748.png)
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Abstract
En 中文
Self-supervised learning (SSL) provides a promising paradigm for hypergraph representation learning without reliance on costly labels. However, existing hypergraph SSL methods predominantly employ contrastive learning with instance-level discrimination, encountering two significant challenges: (1) Unreliable negative sampling, where arbitrarily selected negative samples introduce bias by misclassifying similar and dissimilar pairs; and (2) High computational cost, as effective training requires a large number of negative samples. To address these limitations, we propose SE-HSSL, a hypergraph SSL framework with three sampling-efficient self-supervised objectives. Specifically, two sampling-free objectives based on canonical correlation analysis serve as node- and group-level signals, while a hierarchical membership-level contrastive objective exploits the cascading overlap structure of hypergraphs. Beyond these challenges, deep hypergraph models are prone to biased predictions against groups defined by sensitive attributes (e.g., gender and race). We theoretically show that imbalanced group contributions during hypergraph message passing amplify such biases. To address this, we propose FairHSSL, a fairnessaware extension of SE-HSSL with a two-level debiasing augmentation strategy. Specifically, we construct a fair hypergraph view via complementary feature- and structure-level adjustments. At the feature level, orthogonal projection removes sensitive information from node representations; at the structure level, rebalance-based perturbation equalizes group contributions during message passing. By aligning the fair and original views under SSL, the model mitigates bias while preserving informative signals. Extensive experiments on 10 real-world hypergraphs demonstrate the effectiveness and efficiency of SE-HSSL.
Keywords:
Hypergraph
self-supervised learning
efficiency
fairness
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