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Adaptive Temperature-Enhanced Hypergraph Contrastive Learning with Reliable Data Sampling Augmentation

Junzheng Li,Hongtao Yu,Ruiyang Huang,Suchang Yang

Published 2025 in International Conference on Trust, Security and Privacy in Computing and Communications

ABSTRACT

Inspired by the success of contrastive learning in graphs, researchers have begun exploring the potential of hypergraph contrastive learning. However, existing methods exhibit the following limitations when modeling higher-order relationships in unlabeled data: (1) a failure to fully consider the reliability of different nodes and their critical role in contrastive learning; (2) most methods overlook the importance of temperature coefficients in the contrastive learning process. To address these issues, we propose a hypergraph contrastive learning method based on high-reliability node awareness and adaptive temperature coefficients (ADT-HCL). First, we design a high-reliability node awareness technique based on perturbation invariance. This technique can quantify node reliability and filter out nodes with high noise robustness from both ordinary graph and hypergraph perspectives. Additionally, we propose a temperature coefficient adaptation strategy based on sample pair similarity. This strategy dynamically adjusts the temperature coefficients in contrastive learning, thereby enhancing the model’s ability to distinguish between sample pairs. We conducted extensive experiments on multiple mainstream datasets, and the results demonstrate that the proposed ADT-HCL method achieves outstanding performance in hypergraph node classification tasks.

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