用图神经网络提升社交网络异常用户识别能力
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks
- 融合GAE与GAT，动态聚合邻居特征并评估重构误差
- 在Facebook数据集上AUC等指标均优于传统方法
- 适合金融风控与社会安全领域应用
本研究提出一种基于图神经网络的社交网络用户画像风险定价异常检测方法，旨在提升社交环境中异常用户识别能力。针对传统方法在社交网络数据建模上的局限性，本文结合图自编码器（GAE）与图注意力网络（GAT），通过动态聚合邻居特征并评估重构误差，实现异常用户精准检测。实验使用Facebook Page-Page Network数据集，与VAE、GNN、Transformer和GAE对比，结果表明所提方法在AUC、F1-score、精确率和召回率四项指标上表现最优，验证了其有效性。此外，本文还探讨了模型在大规模数据下的计算效率，并展望未来结合自监督学习、联邦学习等技术以增强风险评估的鲁棒性与隐私保护能力。研究成果可为金融风险控制、社会治理等领域提供高效异常检测解决方案。
原文摘要 · Abstract (English)
This study proposes a risk pricing anomaly detection method for social network user portraits based on graph neural networks (GNNs), aiming to improve the ability to identify abnormal users in social network environments. In view of the limitations of traditional methods in social network data modeling, this paper combines graph autoencoders (GAEs) and graph attention networks (GATs) to achieve accurate detection of abnormal users through dynamic aggregation of neighbor features and reconstruction error evaluation. The Facebook Page-Page Network dataset is used in the experiment and compared with VAE, GNN, Transformer and GAE. The results show that the proposed method achieves the best performance in AUC, F1-score, Precision and Recall, verifying its effectiveness. In addition, this paper explores the computational efficiency of the model in large-scale data and looks forward to combining self-supervised learning, federated learning, and other technologies in the future to improve the robustness and privacy protection of risk assessment. The research results can provide efficient anomaly detection solutions for financial risk control, social security management, and other fields.
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