超图自监督学习与采样高效信号
Hypergraph Self-supervised Learning with Sampling-efficient Signals
AI总结：
针对现有超图自监督对比学习负采样任意与成本高的问题，提出SE-HSSL框架，采用典型相关分析无采样信号及层次成员级对比目标，在7个真实超图上实现更优效果与效率。
摘要：
自监督学习（SSL）为超图上的表示学习提供了一种无需昂贵标签的有前景的替代方案。然而，现有的超图SSL模型大多基于对比方法，采用实例级判别策略，存在两个显著局限：（1）它们任意选择负样本，在判定相似与不相似样本对时不可靠，导致训练偏差；（2）它们通常需要大量负样本，造成昂贵的计算成本。为解决上述问题，我们提出了SE-HSSL，一个具有三种采样高效自监督信号的超图SSL框架。具体而言，我们引入了两个无采样目标，利用典型相关分析作为节点级和组级自监督信号。此外，我们开发了一种新颖的层次成员级对比目标，其动机源于超图中的级联重叠关系，这可以进一步减少成员采样偏差并提高样本利用效率。通过在7个真实世界超图上的全面实验，我们证明了我们的方法在有效性和效率方面均优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
