mlsanityData to 14 Oct 2026

A Review on Graph Neural Network Methods in Financial Applications

arXiv 2111.15367 ↗First posted 27 Nov 2021q-fin.ST cs.LG stat.APcited by 78

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Abstract (arXiv)

With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations. Due to the complexity and volatility of the financial market, the graph constructed on the financial data is often heterogeneous or time-varying, which imposes challenges on modeling technology. Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks. In this work, we provide a comprehensive review of GNN models in recent financial context. We first categorize the commonly-used financial graphs and summarize the feature processing step for each node. Then we summarize the GNN methodology for each graph type, application in each area, and propose some potential research areas.

Key facts. Cited by 78 papers in the map (data through 14 Oct 2026). Builds on 30 of its 91 references that are in the map. First posted on arXiv 27 Nov 2021.

Builds on

30 of 91 references.

References · 30

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18 more
  1. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 385
  2. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  3. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
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  7. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 96
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  9. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
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  15. 2019BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchaincited by 12
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 11; 2023: 19; 2024: 20; 2025: 15; 2026: 13.

78 papers.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  3. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  4. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 103
  5. 2022Debiasing Graph Neural Networks via Learning Disentangled Causal Substructurecited by 83
  6. 2022What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoderscited by 82
  7. 2023Realistic Synthetic Financial Transactions for Anti-Money Laundering Modelscited by 50
  8. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  9. 2024Graph Condensation: A Surveycited by 23
  10. 2024Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?cited by 23
  11. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  12. 2023Graph Neural Network-based EEG Classification: A Surveycited by 21
38 more
  1. 2023Graph Meets LLMs: Towards Large Graph Modelscited by 20
  2. 2022PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphscited by 18
  3. 2023Continual Graph Learning: A Surveycited by 18
  4. 2022More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networkscited by 15
  5. 2023Graph Neural Networks for Text Classification: A Surveycited by 15
  6. 2023ProGAP: Progressive Graph Neural Networks with Differential Privacy Guaranteescited by 12
  7. 2024Continual Learning on Graphs: A Surveycited by 12
  8. 2024GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applicationscited by 10
  9. 2024On The Temporal Domain of Differential Equation Inspired Graph Neural Networkscited by 9
  10. 2025From Deep Learning to LLMs: A survey of AI in Quantitative Investmentcited by 9
  11. 2024Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-trainingcited by 7
  12. 2025Residual connections provably mitigate oversmoothing in graph neural networkscited by 7
  13. 2023GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hintcited by 6
  14. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  15. 2024Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Predictioncited by 5
  16. 2023E2EAI: End-to-End Deep Learning Framework for Active Investingcited by 4
  17. 2023Generative Explanations for Graph Neural Network: Methods and Evaluationscited by 4
  18. 2024Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detectioncited by 4
  19. 2024MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRUcited by 4
  20. 2022EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Modelcited by 3
  21. 2023Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Surveycited by 3
  22. 2024A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal Viewcited by 3
  23. 2024Stabilizing the Kumaraswamy Distributioncited by 3
  24. 2024Computing Systemic Risk Measures with Graph Neural Networkscited by 3
  25. 2025Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)cited by 3
  26. 2026CITED: A Decision Boundary-Aware Signature for GNNs Towards Model Extraction Defensecited by 3
  27. 2026Universality and Approximation Rates of Graph Neural Networks with Random Featurescited by 3
  28. 2022Factor Investing with a Deep Multi-Factor Modelcited by 2
  29. 2023TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twittercited by 2
  30. 2023Unstructured and structured data: Can we have the best of both worlds with large language models?cited by 2
  31. 2023On the Inherent Privacy Properties of Discrete Denoising Diffusion Modelscited by 2
  32. 2024A Benchmark for Fairness-Aware Graph Learningcited by 2
  33. 2024BlockScan: Detecting Anomalies in Blockchain Transactionscited by 2
  34. 2024Natural Language Counterfactual Explanations for Graphs Using Large Language Modelscited by 2
  35. 2025Structural Alignment Improves Graph Test-Time Adaptationcited by 2
  36. 2025QuantBench: Benchmarking AI Methods for Quantitative Investmentcited by 2
  37. 2026Re-understanding Graph Unlearning through Memorizationcited by 2
  38. 2022Dimensionality Reduction Meets Message Passing for Graph Node Embeddingscited by 1

50 most cited of 78 All 78 (JSON)