# Ponzi Detection Eval

> This benchmark evaluates the effectiveness of feature augmentation modules for detecting Ponzi scheme accounts on the Ethereum blockchain. It probes a model's ability to classify account nodes as legitimate or malicious based on transaction graph structures and temporal behavior patterns. Use when the user wants to benchmark on Ethereum Ponzi dataset, or asks about evaluating this task. Reports micro-F1.

- Skill: `qhjqhj00/ponzi-detection-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ponzi-detection-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ponzi-detection-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/ponzi-detection-eval

---


# ponzi-detection-eval

> Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum — Jin et al. (2022) (arXiv:2210.16863, 2022)

## What this evaluates

This benchmark evaluates the effectiveness of feature augmentation modules for detecting Ponzi scheme accounts on the Ethereum blockchain. It probes a model's ability to classify account nodes as legitimate or malicious based on transaction graph structures and temporal behavior patterns.

## Datasets

- **Ethereum Ponzi dataset** — total ?; splits: test (-1)

## Metrics

- `micro-F1` **(primary)** — range: [0, 1]
  - Micro-averaged F1 score computed across all account nodes by aggregating true positives, false positives, and false negatives globally before calculating precision and recall.

## Input / output format

**Input**: Account node features (derived from manual engineering, graph embeddings like Line/DeepWalk/Node2Vec/Trans2Vec, or GNN initial representations) and the underlying Ethereum transaction graph structure.

**Output**: Binary classification label per account node (Ponzi vs. Normal).

## Scoring recipe

```python
# Pseudo-code for evaluation protocol
all_preds = []
all_labels = []
for fold in range(5):
    for seed in range(5):
        train_data, test_data = split_data(fold, seed)
        model.fit(train_data)
        preds = model.predict(test_data)
        all_preds.extend(preds)
        all_labels.extend(test_data.labels)
micro_f1 = compute_micro_f1(all_labels, all_preds)
```

## Common pitfalls

- The test set contains only 38 Ponzi accounts, which severely limits statistical power and may cause high variance in reported scores.
- Performance is averaged over 25 runs (5 folds × 5 seeds); reporting only the mean can mask instability across different random seeds.
- The augmentation module is applied as a drop-in feature enhancer without modifying the base classifier, so gains are inherently constrained by the base method's representational capacity.

## Evidence (verbatim from paper)

> For all methods, we repeat 5-fold cross-validation five times with five different random seeds and report the average micro-F1 score over 5 × 5 = 25 experiments.

## Citation

```bibtex
@misc{jin2022ponzidetection,
  title={Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum},
  author={Jin et al. (2022)},
  year={2022},
  note={arXiv:2210.16863}
}
```

- arXiv: 2210.16863