mlsanityData to 14 Oct 2026

Robust Graph Neural Networks for Stability Analysis in Dynamic Networks

arXiv 2411.11848 ↗First posted 29 Oct 2024q-fin.ST cs.LGcited by 8

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Abstract (arXiv)

In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 29 Oct 2024.

Builds on

6 of 11 references.

References · 6

  1. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  2. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  3. 2024Real-Time Pill Identification for the Visually Impaired Using Deep Learningcited by 21
  4. 2024Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimizationcited by 12
  5. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  6. 2024An Efficient Recommendation Model Based on Knowledge Graph Attention-Assisted Network (KGATAX)cited by 3

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Cited by year

Citing papers per year: 2024: 5; 2025: 3.

8 papers.

  1. 2025Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Datacited by 11
  2. 2024An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reductioncited by 9
  3. 2024Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Miningcited by 8
  4. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
  5. 2025A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Modelscited by 7
  6. 2024Dynamic Scheduling Strategies for Resource Optimization in Computing Environmentscited by 6
  7. 2024Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXtcited by 5
  8. 2025Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Modelscited by 4