mlsanityData to 14 Oct 2026

Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks

arXiv 2503.19380 ↗First posted 25 Mar 2025cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 25 Mar 2025.

Builds on

9 of 24 references.

References · 9

  1. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  2. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  3. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  4. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  5. 2024Rumor Detection with a novel graph neural network approachcited by 15
  6. 2025Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusioncited by 11
  7. 2024Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applicationscited by 9
  8. 2025A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Predictioncited by 9
  9. 2025A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Modelscited by 7

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Cited by year

Citing papers per year: 2025: 9.

9 papers.

  1. 2025Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Modelscited by 8
  2. 2025Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQNcited by 6
  3. 2025Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimationcited by 6
  4. 2025Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuningcited by 5
  5. 2025Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactionscited by 4
  6. 2025Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategiescited by 4
  7. 2025Structured Gradient Guidance for Few-Shot Adaptation in Large Language Modelscited by 3
  8. 2025Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learningcited by 2
  9. 2025DeepSORT-Driven Visual Tracking Approach for Gesture Recognition in Interactive Systemscited by 1