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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - graph-ml
+tags:
+  - intrusion-detection
+  - CAN-bus
+  - graph-neural-networks
+  - knowledge-distillation
+pretty_name: GraphIDS Paper Data
+---
+
+# GraphIDS — Paper Data
+
+Evaluation artifacts for "Adaptive Fusion of Graph-Based Ensembles for Automotive Intrusion Detection".
+
+Consumed by [kd-gat-paper](https://github.com/frenken-lab/kd-gat-paper) via `data/pull_data.py`.
+
+## Schema Contract
+
+**If you change column names or file structure, `pull_data.py` will fail.**
+The input schema is enforced in `pull_data.py:INPUT_SCHEMA`.
+
+### metrics.parquet
+
+Per-model evaluation metrics across all runs.
+
+| Column | Type | Description |
+|---|---|---|
+| `run_id` | str | Run identifier, e.g. `hcrl_sa/eval_large_evaluation` |
+| `model` | str | Model name: `gat`, `vgae`, `fusion` |
+| `accuracy` | float | Classification accuracy |
+| `precision` | float | Precision |
+| `recall` | float | Recall (sensitivity) |
+| `f1` | float | F1 score |
+| `specificity` | float | Specificity (TNR) |
+| `balanced_accuracy` | float | Balanced accuracy |
+| `mcc` | float | Matthews correlation coefficient |
+| `fpr` | float | False positive rate |
+| `fnr` | float | False negative rate |
+| `auc` | float | Area under ROC curve |
+| `n_samples` | float | Number of evaluation samples |
+| `dataset` | str | Dataset name: `hcrl_sa`, `hcrl_ch`, `set_01`–`set_04` |
+
+### embeddings.parquet
+
+2D UMAP projections of graph embeddings per model.
+
+| Column | Type | Description |
+|---|---|---|
+| `run_id` | str | Run identifier |
+| `model` | str | Model that produced the embedding: `gat`, `vgae` |
+| `x` | float | UMAP dimension 1 |
+| `y` | float | UMAP dimension 2 |
+| `label` | int | Ground truth: 0 = normal, 1 = attack |
+
+### cka_similarity.parquet
+
+CKA similarity between teacher and student layers (KD runs only).
+
+| Column | Type | Description |
+|---|---|---|
+| `run_id` | str | Run identifier (only `*_kd` runs) |
+| `dataset` | str | Dataset name |
+| `teacher_layer` | str | Teacher layer name, e.g. `Teacher L1` |
+| `student_layer` | str | Student layer name, e.g. `Student L1` |
+| `similarity` | float | CKA similarity score (0–1) |
+
+### dqn_policy.parquet
+
+DQN fusion weight (alpha) per evaluated graph.
+
+| Column | Type | Description |
+|---|---|---|
+| `run_id` | str | Run identifier |
+| `dataset` | str | Dataset name |
+| `scale` | str | Model scale: `large`, `small` |
+| `has_kd` | int | Whether KD was used: 0 or 1 |
+| `action_idx` | int | Graph index within the evaluation set |
+| `alpha` | float | Fusion weight (0 = full VGAE, 1 = full GAT) |
+
+**Note:** Lacks per-graph `label` and `attack_type`. The paper figure needs these fields joined from evaluation results. This is a known gap — see `pull_data.py` skip message.
+
+### recon_errors.parquet
+
+VGAE reconstruction error per evaluated graph.
+
+| Column | Type | Description |
+|---|---|---|
+| `run_id` | str | Run identifier |
+| `error` | float | Scalar reconstruction error |
+| `label` | int | Ground truth: 0 = normal, 1 = attack |
+
+**Note:** Single scalar error — no per-component decomposition (Node Recon, CAN ID, Neighbor, KL). The paper figure needs the component breakdown. This is a known gap.
+
+### attention_weights.parquet
+
+Mean GAT attention weights aggregated per head.
+
+| Column | Type | Description |
+|---|---|---|
+| `run_id` | str | Run identifier |
+| `sample_idx` | int | Graph sample index |
+| `label` | int | Ground truth: 0 = normal, 1 = attack |
+| `layer` | int | GAT layer index |
+| `head` | int | Attention head index |
+| `mean_alpha` | float | Mean attention weight for this head |
+
+**Note:** Aggregated per-head, not per-edge. The paper figure needs per-edge attention weights with node positions. This is a known gap.
+
+### graph_samples.json
+
+Raw CAN bus graph instances with node/edge features.
+
+Top-level keys: `schema_version`, `exported_at`, `data`, `feature_names`.
+
+Each item in `data`:
+- `dataset`: str — dataset name
+- `label`: int — 0/1
+- `attack_type`: int — attack type code
+- `attack_type_name`: str — human-readable name
+- `nodes`: list of `{id, features, node_y, node_attack_type, node_attack_type_name}`
+- `links`: list of `{source, target, edge_features}`
+- `num_nodes`, `num_edges`: int
+- `id_entropy`, `stats`: additional metadata
+
+### metrics/*.json
+
+Per-configuration evaluation results. 18 files covering 6 datasets x 3 configs (large, small, small_kd).
+
+Each file: `{schema_version, exported_at, data: [{model, scenario, metric_name, value}]}`
+
+Currently only contains `val` scenario — cross-dataset test scenarios are not yet exported.
+
+### Other files
+
+| File | Description |
+|---|---|
+| `leaderboard.json` | Cross-dataset model comparison (all metrics, all runs) |
+| `model_sizes.json` | Parameter counts per model type and scale |
+| `training_curves.parquet` | Loss/accuracy curves over training epochs |
+| `graph_statistics.parquet` | Per-graph structural statistics |
+| `datasets.json` | Dataset metadata |
+| `runs.json` | Run metadata |
+| `kd_transfer.json` | Knowledge distillation transfer metrics |
+
+## Run ID Convention
+
+Format: `{dataset}/{eval_config}`
+
+- Datasets: `hcrl_sa`, `hcrl_ch`, `set_01` through `set_04`
+- Configs: `eval_large_evaluation`, `eval_small_evaluation`, `eval_small_evaluation_kd`
+
+The paper defaults to `hcrl_sa/eval_large_evaluation` for main results and `hcrl_sa/eval_small_evaluation_kd` for CKA.
+
+## Known Gaps
+
+These files need richer exports from the KD-GAT pipeline:
+
+1. **dqn_policy.parquet** — needs per-graph `label` + `attack_type` columns
+2. **recon_errors.parquet** — needs per-component error decomposition
+3. **attention_weights.parquet** — needs per-edge weights + node positions
+4. **metrics/*.json** — needs cross-dataset test scenario results
+
+Until these are addressed, `pull_data.py` preserves existing committed files for the affected figures.