# Modeconv Anomaly Detection Eval

> Evaluates a graph neural network's ability to detect structural anomalies by reconstructing multivariate time-series sensor data. It measures how well the model captures physical material properties and eigenmode shifts compared to spectral GNN baselines, while also benchmarking computational efficiency. Use when the user wants to benchmark on Luxembourg dataset, Simulated Smart Bridge dataset, or asks about evaluating this task. Reports reconstruction error.

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

---


# modeconv-anomaly-detection-eval

> ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural Behavior — Schaller et al. (2024) (arXiv:2407.00140, 2024)

## What this evaluates

Evaluates a graph neural network's ability to detect structural anomalies by reconstructing multivariate time-series sensor data. It measures how well the model captures physical material properties and eigenmode shifts compared to spectral GNN baselines, while also benchmarking computational efficiency.

## Datasets

- **Luxembourg dataset** — total ?; splits: test (-1)
- **Simulated Smart Bridge dataset** — total ?; splits: test (-1)

## Metrics

- `reconstruction error` **(primary)** — range: other
  - The error between the original multivariate time-series sensor input and the autoencoder's reconstructed output. Optimized via hyperparameter search to ensure fair comparison across models.
- `training time` — range: other
  - Wall-clock duration required to train each model for the fixed number of epochs (50). Used to benchmark computational complexity and runtime efficiency.

## Input / output format

**Input**: Multivariate time-series sensor data structured as a graph, representing physical material properties and sensor correlations over time.

**Output**: Reconstructed multivariate time-series data and anomaly indicators derived from eigenmode shifts via SVD on modal coordinates.

## Scoring recipe

```python
def compute_metrics(predictions, ground_truth, training_time):
    # Calculate reconstruction error (e.g., MSE/MAE between input and output)
    recon_error = np.mean((predictions - ground_truth) ** 2)
    # Record wall-clock training time in seconds
    runtime = training_time
    return {'reconstruction_error': recon_error, 'training_time': runtime}
```

## Common pitfalls

- Hyperparameter tuning was performed via Optuna to minimize reconstruction error across all models, which may obscure dataset-specific optimal configurations.
- Wrappers were manually adapted to make MtGNN and GraphCON compatible with PyTorch Lightning and stable dimension constraints, potentially introducing implementation artifacts.
- Runtime comparisons are hardware-specific (NVIDIA GeForce GTX 1080 Ti) and fixed to 50 epochs, limiting direct cross-platform reproducibility.

## Evidence (verbatim from paper)

> To validate these claims, we compare the results of a graph autoencoder with ModeConv layers trained on the Luxembourg dataset with the results of the same graph autoencoder using ChebConv layers [[24]], which are a type of Spectral Graph Convolution. We also evaluate the results on the Simulated Smart Bridge dataset. ... All of the models have been tuned via a hyperparameter study using optuna [[4]], that aims to improve the reconstruction error for all networks in order to create a fair experimental setup. ... Furthermore, we compare the training time of all the models and conduct a comparative study on the single blocks of the ModeConv approach.

## Citation

```bibtex
@misc{schaller2024modeconv,
  title={ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural Behavior},
  author={Schaller et al. (2024)},
  year={2024},
  note={arXiv:2407.00140}
}
```

- arXiv: 2407.00140