网络与信息安全学报2024，Vol.10Issue(3)：52-65,14.DOI:10.11959/j.issn.2096-109x.2024039
基于SCL-CMM模型的超网络链路预测方法
Hypernetwork link prediction method based on the SCL-CMM model
摘要
Abstract
An effective method for the internal interaction within modeling reality systems is provided by graphs;however,they have been unable to effectively display and capture the high-order heterogeneity that widely exists between multiple entities.Hypergraphs have been recognized for their ability to surpass the limitations imposed by low-order relationships.Hypernetwork link prediction,which involves predicting unknown hyperlinks based on the observed hypergraph structure,has increasingly become a hot topic in network science due to its capacity to fully describe the association patterns of complex systems.Existing methods typically design reasoning models for the entire topology,often overlooking the implicit aggregation characteristics within the network,which leads to an in-complete prediction of hyperlink categories.To address these issues,a coordination matrix minimization model based on hypergraph spectral clustering parser(SCL-CMM)was proposed.Initially,higher-order hypernetworks were mapped into heterogeneous hypergraphs with certain semantics.Subsequently,the spectral clustering parser was employed to extract the structural features of hyperlinks.The original hypergraph was reconstructed into mul-tiple homoprotonic graphs,and the distribution of potential hyperlinks was inferred within the observation space of the subgraph,rather than the entire adjacency space,in order to restore the complete hypernetwork structure.This method federated learned the structural characteristics and aggregation attributes of hypernetworks to model the high-order nonlinear behavior of each subgraph,thereby solving the problems of single category and low precision in het-erogeneous hypergraphs link prediction.Extensive comparative experiments were conducted on nine real datasets,demonstrating that this method significantly outperformed existing methods in terms of AUC score and recall rate.关键词
链路预测/超图/超网络/拓扑结构/聚类Key words
link prediction/hypergraph/hypernetwork/topology/clustering分类
信息技术与安全科学引用本文复制引用
任玉媛,马宏,刘树新,王凯..基于SCL-CMM模型的超网络链路预测方法[J].网络与信息安全学报,2024,10(3):52-65,14.基金项目
中原英才计划项目(6212101510002) Zhongyuan Talent Program(6212101510002) （6212101510002）
