Deep learning-based method for mobile social networks with strong sparsity for link prediction

HE Yadi; LIU Linfeng

doi:10.11959/j.issn.2096-109x.2024044

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Deep learning-based method for mobile social networks with strong sparsity for link prediction

Papers | 更新时间：2024-08-14

- Deep learning-based method for mobile social networks with strong sparsity for link prediction
- Chinese Journal of Network and Information Security Vol. 10, Issue 3, Pages: 117-129(2024)
- 作者机构：
  
  南京邮电大学计算机学院，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62272237)
- DOI：10.11959/j.issn.2096-109x.2024044
  CLC：
- Received：21 January 2024，
  
  Revised：2024-04-09，
  
  Published：25 June 2024
- 稿件说明：
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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.

HE Y D, LIU L F. Deep learning-based method for mobile social networks with strong sparsity for link prediction[J]. Chinese Journal of Network and Information Security, 2024, 10(3): 117-129.
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. DOI： 10.11959/j.issn.2096-109x.2024044.

HE Y D, LIU L F. Deep learning-based method for mobile social networks with strong sparsity for link prediction[J]. Chinese Journal of Network and Information Security, 2024, 10(3): 117-129. DOI： 10.11959/j.issn.2096-109x.2024044.

摘要

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

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

Keywords

references

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