计算机技术与发展2024，Vol.34Issue(9)：116-123,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0136
NHCL:一种基于原生结构增强的超图对比学习
NHCL:A Hypergraph Contrastive Learning Based on Native Structure Augmentation
摘要
Abstract
Hypergraph contrastive learning based on self-supervised learning has been extensively studied.However,current hypergraph contrastive learning mostly relies on traditional data augmentation methods used in graph representation learning,which less consider the native structure of hypergraph,and failing to fully exploit higher-order relationships within hypergraph.To address this limitation,a series of data augmentation operations based on the native structure of the hypergraph are proposed,that is,perturbing the hyperedges and nodes in the hypergraph.By studying the inclusion,combination,and intersection relationships between hyperedges and the interactive re-lationships between nodes,we propose a set of fundamental perturbation operations tailored for hyperedges and nodes,and these basic op-erations between hyperedges and nodes are combined to help the model learn.By using basic data enhancement operations and their com-binations,positive and negative sample pairs are generated for hypergraph comparison learning.We employ hypergraph neural networks to learn their representations and encode them while guiding model training with a loss function,which helps the model better capture high-order relationships within hypergraphs.To validate the effectiveness of the proposed method,we conduct node classification experiments on 12 commonly used hypergraph benchmark datasets,including Cora-CA,PubMed,and ModelNet40.The experimental results show that the proposed method outperforms existing two hypergraph self-supervised methods like Self and Con,hypergraph contrastive learning methods like HyperGCL and TriCL,achieving 2%to 7%improvement in node classification accuracy.关键词
超图对比学习/数据增强/超图原生结构/超图神经网络/自监督学习Key words
hypergraph contrastive learning/data augmentation/hypergraph native structure/hypergraph neural networks/self-supervised learning分类
信息技术与安全科学引用本文复制引用
刘宇,侯阿龙,方舒言,高峰,张晓龙..NHCL:一种基于原生结构增强的超图对比学习[J].计算机技术与发展,2024,34(9):116-123,8.基金项目
科技创新2030"新一代人工智能"重大项目(2020AAA0108501) （2020AAA0108501）
国家自然科学基金(62261023) （62261023）
