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

arXiv: https://arxiv.org/abs/2503.19380 · HTML: https://mlsanity.com/paper/2503.19380/

## 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 (most cited first, 9 of 9)

- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges](https://mlsanity.com/paper/2403.04468/) (2024)
- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Rumor Detection with a novel graph neural network approach](https://mlsanity.com/paper/2403.16206/) (2024)
- [Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion](https://mlsanity.com/paper/2502.03664/) (2025)
- [Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications](https://mlsanity.com/paper/2412.18222/) (2024)
- [A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction](https://mlsanity.com/paper/2502.06847/) (2025)
- [A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models](https://mlsanity.com/paper/2502.03386/) (2025)

## Built on by (most cited first, 9 of 9)

- [Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models](https://mlsanity.com/paper/2504.19436/) (2025)
- [Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN](https://mlsanity.com/paper/2504.09344/) (2025)
- [Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation](https://mlsanity.com/paper/2505.07674/) (2025)
- [Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning](https://mlsanity.com/paper/2504.19583/) (2025)
- [Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions](https://mlsanity.com/paper/2504.15491/) (2025)
- [Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies](https://mlsanity.com/paper/2505.06145/) (2025)
- [Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models](https://mlsanity.com/paper/2506.00726/) (2025)
- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)
- [DeepSORT-Driven Visual Tracking Approach for Gesture Recognition in Interactive Systems](https://mlsanity.com/paper/2505.07110/) (2025)

## 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.