Blockchain Fraud Detection Using Ensemble Graph Neural Networks
Bibliographic record
Abstract
Cryptocurrencies like Bitcoin promise secure, decentralized transactions, but their anonymity also attracts illicit activity, posing a challenge to regulators and exchanges in maintaining control. This study tackles fraud detection in Bitcoin's transaction network using the Elliptic dataset, a real-world collection of labeled transactions. We combine three powerful graph neural networks Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Isomorphism Network (GIN) each capturing different patterns in the complex web of blockchain payments. By blending their predictions through ensemble techniques, such as tuned soft voting, we achieve a robust system that detects over 70% of illicit transactions while keeping false alarms below 1%. Our approach balances precision and coverage, making it practical for real-time anti-money laundering efforts. The modular framework adapts easily to new data, paving the way for scalable, reliable monitoring of cryptocurrency fraud.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".