计算机工程2025，Vol.51Issue(6)：360-374,15.DOI:10.19678/j.issn.1000-3428.0068368
融入节点平衡性指数的有向符号网络链路符号预测
Sign Prediction of Links in Signed Directed Networks with Node s Balanced Index
摘要
Abstract
The sign prediction of links in signed directed networks can be used to model many real-life problems;however,sign prediction is a core problem in the field of network science.The main theoretical support for sign prediction algorithms for links in signed directed networks is structural balance theory,which has profound research significance.Real-world networks are complicated.They do not precisely follow the structural balance theory,and different networks have their own unique characteristics.This study first analyzes the basic mechanisms affecting the signs of links and explores the network features reflecting the formation of signs.Next,the study defines the balanced index of a node from each remaining node and integrates its features according to Chiang's prediction method.The amount of feature information increases and the sign prediction of links in signed directed networks is achieved without increasing the computational complexity.The network features are divided into three categories and a logistic regression model is used to train and test different combinations of these features.Experimental results on several real network datasets demonstrate that the model exhibits good generalization ability and the inclusion of the node balance index feature significantly improves the predictive accuracy of the model.Finally,a logistic regression model is used to train and test all network features involved.Experimental comparisons are conducted between the proposed algorithm and the current advanced sign prediction link algorithm to validate its effectiveness.关键词
有向符号网络/节点平衡性指数/链路符号预测/有监督学习/逻辑回归Key words
signed directed networks/node's balanced index/sign prediction of links/supervised learning/logistic regression分类
信息技术与安全科学引用本文复制引用
李树鹏,董继远,刘娟..融入节点平衡性指数的有向符号网络链路符号预测[J].计算机工程,2025,51(6):360-374,15.基金项目
贵州省教育厅自然科学研究项目-高等学校青年科技人才成长项目(黔教技[2024]78号) （黔教技[2024]78号）
国家自然科学基金(12261016). （12261016）
