面向强稀疏性移动社交网络的链路预测深度学习方法
链路预测是利用深度学习技术分析网络数据,挖掘网络中潜在的节点关系,通常应用于网络安全、信息挖掘等领域.通过预测网络中节点间的链路,可以识别社交工程攻击、欺诈行为和隐私泄露风险.但移动社交网络的拓扑结构随时间变化,链路稀疏,影响预测准确性.为了解决移动社交网络中链路预测的强稀疏性问题,提出基于深度学习的预测方法,即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,DLMSS-LP).该方法综合运用了图自编码器(...
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| Published in | 网络与信息安全学报 Vol. 10; no. 3; pp. 117 - 129 |
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| Main Authors | , |
| Format | Journal Article |
| Language | Chinese |
| Published |
南京邮电大学计算机学院,江苏 南京 210023
25.06.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2096-109X |
| DOI | 10.11959/j.issn.2096-109x.2024044 |
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| Abstract | 链路预测是利用深度学习技术分析网络数据,挖掘网络中潜在的节点关系,通常应用于网络安全、信息挖掘等领域.通过预测网络中节点间的链路,可以识别社交工程攻击、欺诈行为和隐私泄露风险.但移动社交网络的拓扑结构随时间变化,链路稀疏,影响预测准确性.为了解决移动社交网络中链路预测的强稀疏性问题,提出基于深度学习的预测方法,即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,DLMSS-LP).该方法综合运用了图自编码器(graph auto-encoder,GAE)、特征矩阵聚合技术以及多层长短期记忆网络(long short-term memory,LSTM),旨在降低了模型的学习成本,更有效地处理高维和非线性的网络结构,并且捕捉移动社交网络中的时序动态变化,进而增强模型对现有链路生成可能性的预测能力.对比其他方法在AUC(area under curve)和ER(error rate)指标上有明显提升,体现了模型对不确定链路预测的高准确率和强鲁棒性. |
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| AbstractList | 链路预测是利用深度学习技术分析网络数据,挖掘网络中潜在的节点关系,通常应用于网络安全、信息挖掘等领域.通过预测网络中节点间的链路,可以识别社交工程攻击、欺诈行为和隐私泄露风险.但移动社交网络的拓扑结构随时间变化,链路稀疏,影响预测准确性.为了解决移动社交网络中链路预测的强稀疏性问题,提出基于深度学习的预测方法,即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,DLMSS-LP).该方法综合运用了图自编码器(graph auto-encoder,GAE)、特征矩阵聚合技术以及多层长短期记忆网络(long short-term memory,LSTM),旨在降低了模型的学习成本,更有效地处理高维和非线性的网络结构,并且捕捉移动社交网络中的时序动态变化,进而增强模型对现有链路生成可能性的预测能力.对比其他方法在AUC(area under curve)和ER(error rate)指标上有明显提升,体现了模型对不确定链路预测的高准确率和强鲁棒性. |
| Abstract_FL | 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. |
| Author | 刘林峰 何亚迪 |
| AuthorAffiliation | 南京邮电大学计算机学院,江苏 南京 210023 |
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| Author_FL | LIU Linfeng HE Yadi |
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| Author_xml | – sequence: 1 fullname: 何亚迪 – sequence: 2 fullname: 刘林峰 |
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| Keywords | 移动社交网络 strong sparsity deep learning 深度学习 link prediction mobile social networks 链路预测 强稀疏性 |
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| Title | 面向强稀疏性移动社交网络的链路预测深度学习方法 |
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