网络与信息安全学报2024，Vol.10Issue(3)：117-129,13.DOI:10.11959/j.issn.2096-109x.2024044
面向强稀疏性移动社交网络的链路预测深度学习方法
Deep learning-based method for mobile social networks with strong sparsity for link prediction
摘要
Abstract
Link prediction,the process of uncovering potential relationships between nodes in a network through the use of deep learning techniques,is commonly applied in fields such as network security and information mining.It has been utilized to identify social engineering attacks,fraudulent activities,and privacy breach risks by predicting links between nodes within a network.However,the topology of mobile social networks is subject to change over time,and the sparsity of links affects the accuracy of predictions.To address the issue of strong sparsity in link prediction for mobile social networks,a deep learning-based prediction method named DLMSSLP(deep learning-based method for mobile social networks with strong sparsity for link prediction)was developed.This method was designed to employ a combination of a Graph Auto-Encoder(GAE),feature matrix aggregation,and multi-layer long short-term memory networks(LSTM).It aimed to reduce the learning cost of the model,process high-dimensional and nonlinear network structures more effectively,and capture the temporal dynamics within mobile social networks,thereby enhancing the model's predictive capability for the generation of existing links.When compared to other methods,DLMSSLP demonstrated significant improvements in AUC and ER metrics,showcasing the model's high accuracy and robustness in predicting uncertain links.关键词
链路预测/移动社交网络/强稀疏性/深度学习Key words
link prediction/mobile social networks/strong sparsity/deep learning引用本文复制引用
何亚迪,刘林峰..面向强稀疏性移动社交网络的链路预测深度学习方法[J].网络与信息安全学报,2024,10(3):117-129,13.基金项目
国家自然科学基金(62272237) The National Natural Science Foundation of China(62272237) （62272237）
