mlsanityData to 7 Oct 2026

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection

arXiv 2207.03579 ↗First posted 30 Jun 2022cs.SI cs.AIcited by 63

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Abstract (arXiv)

Graph Anomaly Detection (GAD) has recently become a hot research spot due to its practicability and theoretical value. Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is fundamental work. Thus, this paper present DGraph, a real-world dynamic graph in the finance domain. DGraph overcomes many limitations of current GAD datasets. It contains about 3M nodes, 4M dynamic edges, and 1M ground-truth nodes. We provide a comprehensive observation of DGraph, revealing that anomalous nodes and normal nodes generally have different structures, neighbor distribution, and temporal dynamics. Moreover, it suggests that unlabeled nodes are also essential for detecting fraudsters. Furthermore, we conduct extensive experiments on DGraph. Observation and experiments demonstrate that DGraph is propulsive to advance GAD research and enable in-depth exploration of anomalous nodes.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). Builds on 24 of its 60 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

24 of 60 references.

References · 24

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  7. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  8. 2016Variational Graph Auto-Encoderscited by 1,373
  9. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
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  11. 2020Inductive Representation Learning on Temporal Graphscited by 454
  12. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
12 more
  1. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  2. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  3. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  4. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  5. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 158
  6. 2019Deep Anomaly Detection with Deviation Networkscited by 152
  7. 2013From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviewscited by 107
  8. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  9. 2022PyGOD: A Python Library for Graph Outlier Detectioncited by 31
  10. 2014Link Prediction in Social Networks: the State-of-the-Artcited by 22
  11. 2021How effective are Graph Neural Networks in Fraud Detection for Network Data?cited by 2
  12. 2013Reciprocal versus Parasocial Relationships in Online Social Networkscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 12; 2024: 25; 2025: 8; 2026: 16.

63 papers.

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  1. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
  2. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 137
  3. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  4. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  5. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  6. 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphscited by 26
  7. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  8. 2024A survey of dynamic graph neural networkscited by 21
  9. 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysiscited by 13
  10. 2023Fine-tuning Graph Neural Networks by Preserving Graph Generative Patternscited by 13
  11. 2024Node-Time Conditional Prompt Learning In Dynamic Graphscited by 11
  12. 2024SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detectioncited by 11
38 more
  1. 2024Anomaly Detection Based on Isolation Mechanisms: A Surveycited by 10
  2. 2023Graph Propagation Transformer for Graph Representation Learningcited by 9
  3. 2023BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networkscited by 9
  4. 2024DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detectorcited by 9
  5. 2023SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embeddingcited by 7
  6. 2024SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spacescited by 7
  7. 2025K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecastingcited by 7
  8. 2023BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detectioncited by 6
  9. 2024Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graphcited by 6
  10. 2024AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Modelscited by 5
  11. 2022Graph Learning and Its Advancements on Large Language Models: A Holistic Surveycited by 4
  12. 2023Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Networkcited by 4
  13. 2024Temporal Graph Networks for Graph Anomaly Detection in Financial Networkscited by 4
  14. 2025CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detectioncited by 4
  15. 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learningcited by 4
  16. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  17. 2024Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectoriescited by 3
  18. 2023Anomaly Detection in Networks via Score-Based Generative Modelscited by 2
  19. 2024UMGAD: Unsupervised Multiplex Graph Anomaly Detectioncited by 2
  20. 2023DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Riskcited by 1
  21. 2023Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Modelscited by 1
  22. 2024Chromosomal Structural Abnormality Diagnosis by Homologous Similaritycited by 1
  23. 2024Graph Pre-Training Models Are Strong Anomaly Detectorscited by 1
  24. 2024TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scalecited by 1
  25. 2022Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Networkcited by 0
  26. 2024Detecting Anomalies in Dynamic Graphs via Memory enhanced Normalitycited by 0
  27. 2024Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learningcited by 0
  28. 2024Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networkscited by 0
  29. 2024Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Powercited by 0
  30. 2024High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approachcited by 0
  31. 2025CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detectioncited by 0
  32. 2025Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detectioncited by 0
  33. 2025PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graphcited by 0
  34. 2025CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervisioncited by 0
  35. 2025The Role of Community Detection Methods in Performance Variations of Graph Mining Taskscited by 0
  36. 2026Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detectioncited by 0
  37. 2026Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detectioncited by 0
  38. 2026TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detectioncited by 0

50 most cited of 63 All 63 (JSON)