# Fraudster Group Detection Eval

> Evaluates a model's ability to detect fraudulent reviewer groups by analyzing spatio-temporal co-review patterns. It probes the model's capacity to distinguish genuine groups from coordinated fraudster groups using graph representation learning and temporal modeling. Use when the user wants to benchmark on Yelp, Amazon, or asks about evaluating this task. Reports F1-value.

- Skill: `qhjqhj00/fraudster-group-detection-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/fraudster-group-detection-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/fraudster-group-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/fraudster-group-detection-eval

---


# fraudster-group-detection-eval

> Spatio-Temporal Graph Representation Learning for Fraudster Group Detection — Shehnepoor et al. (2022) (arXiv:2201.02621, 2022)

## What this evaluates

Evaluates a model's ability to detect fraudulent reviewer groups by analyzing spatio-temporal co-review patterns. It probes the model's capacity to distinguish genuine groups from coordinated fraudster groups using graph representation learning and temporal modeling.

## Datasets

- **Yelp** — total 9952; splits: train (-1), test (-1)
- **Amazon** — total 2194; splits: train (-1), test (-1)

## Metrics

- `Precision` — range: [0, 1]
  - Precision = TP / (TP + FP), where TP is the number of true positive samples and FP is the number of false positive samples.
- `Recall` — range: [0, 1]
  - Recall = TP / (TP + FN), where FN is the number of false negative samples.
- `F1-value` **(primary)** — range: [0, 1]
  - F1-value = 2 * precision * recall / (precision + recall).

## Input / output format

**Input**: Candidate reviewer groups represented as spatio-temporal graphs derived from co-review interactions within 28-day windows, along with reviewer/item metadata and ratings.

**Output**: Binary classification label for each candidate group: 'genuine' or 'fraudster'.

## Scoring recipe

```python
def calculate_metrics(predictions, gold):
    tp = sum(1 for p, g in zip(predictions, gold) if p == 1 and g == 1)
    fp = sum(1 for p, g in zip(predictions, gold) if p == 1 and g == 0)
    fn = sum(1 for p, g in zip(predictions, gold) if p == 0 and g == 1)
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
    return precision, recall, f1
```

## Common pitfalls

- The evaluation is performed at the group level, not the individual reviewer level.
- Temporal modeling performance degrades on the Amazon dataset due to sparse group data in early time windows.
- Clustering is an optional post-processing step; omitting it changes the precision/recall trade-off by retaining outlier reviewers.

## Evidence (verbatim from paper)

> For evaluation, we used 80% of the data for training and 20% for testing. We used three well-known metrics to evaluate the performance of the proposed approach. First, precision: Precision = TP/(TP+FP) where TP is the number of true positive samples and FP is the number of false positive samples. We also use recall: Recall = TP/(TP+FN) where FN is the number of false negative samples. Finally, we also use the F1-value: F1-value = 2*precision*recall/(precision+recall)

## Citation

```bibtex
@misc{shehnepoor2022spatiotemporal,
  title={Spatio-Temporal Graph Representation Learning for Fraudster Group Detection},
  author={Shehnepoor et al. (2022)},
  year={2022},
  note={arXiv:2201.02621}
}
```

- arXiv: 2201.02621