Hypergraph Self-supervised Learning with Sampling-efficient Signals

• URL: http://arxiv.org/abs/2404.11825v1
• Date: Thu, 18 Apr 2024 01:14:50 GMT
• Title: Hypergraph Self-supervised Learning with Sampling-efficient Signals
• Authors: Fan Li, Xiaoyang Wang, Dawei Cheng, Wenjie Zhang, Ying Zhang, Xuemin Lin,
• Abstract summary: Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. Existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discrimination strategy. We propose SE-HSSL, a hypergraph SSL framework with three sampling-efficient self-supervised signals.
• Score: 36.670771080732486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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