Hypergraph Self-supervised Learning with Sampling-efficient Signals

arXiv:2404.11825v1 Announce Type: new
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 …

abstract arxiv cs.lg discrimination however hypergraph instance labels limitations negative representation representation learning samples sampling self-supervised learning ssl strategy supervised learning type