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safe-graph/graph-fraud-detection-papers
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
1.8k 293 +0/wk
GitHub
academic-publications anomaly-detection awsome-list data-mining data-science dataset deep-learning foundation-models fraud-detection graph-algorithms graph-convolutional-networks graph-neural-networks
Trend
3
Star & Fork Trend (21 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
safe-graph/graph-fraud-detection-papers has +0 stars this period . 7-day velocity: 0.2%.
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| Metric | graph-fraud-detection-papers | pytorch-segmentation | free-ai-resources | MimicKit |
|---|---|---|---|---|
| Stars | 1.8k | 1.8k | 1.8k | 1.8k |
| Forks | 293 | 393 | 320 | 231 |
| Weekly Growth | +0 | +0 | +0 | +7 |
| Language | N/A | Jupyter Notebook | N/A | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | N/A | MIT | MIT | Apache-2.0 |
Capability Radar vs pytorch-segmentation
graph-fraud-detection-papers
pytorch-segmentation
Maintenance Activity 97
Last code push 12 days ago.
Community Engagement 81
Fork-to-star ratio: 16.2%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 30
No clear license detected — proceed with caution.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.