Datasets:

lexuanbach
/

score-diffusion-predictions

---
language: en
pretty_name: Score Diffusion Cached Predictions
size_categories:
- 100K<n<1M
tags:
- graph-learning
- conformal-prediction
- logits
---
# Score diffusion: cached classifier predictions

Public prediction cache for **When Does Score Diffusion Help Graph Conformal Prediction?** by Xuan-Bach Le.

**These files contain logits, split indices and training metadata. They are not model weights or loadable trained-model checkpoints.** The original training script did not save a state_dict. They support recalibration and evaluation on the stored graphs, but cannot run inference on new graphs.
Code, data, supplementary PDF and reproduction instructions: https://github.com/lexuanbach/score-diffusion-artifact
## Contents
`predictions.tar.gz` contains 330 `.pt` files under `results/preds/`: 13 benchmark graphs × 5 classifiers (GCN, GAT, GraphSAGE, GCNII, MLP) × 5 seeds, plus 5 GCN temporal-split caches for ogbn-arxiv. Each dictionary has `logits`, `train_idx`, `val_idx`, and `info`. `info` identifies the dataset, model, seed, split and training summary.
Archive SHA-256: `5f7d44436735100afe708ecc31efdaca7372d736fe3ffed4a08814c2ee8777b5`.
`predictions-files.json` records the size and SHA-256 of every extracted file. This is the same archive published in GitHub release v1.0.0.
## Download into the artifact
From the cloned artifact directory:
```bash
curl -fL https://huggingface.co/datasets/lexuanbach/score-diffusion-predictions/resolve/main/predictions.tar.gz -o predictions.tar.gz
python - <<'PYCODE'
import hashlib, tarfile
from pathlib import Path
p = Path('predictions.tar.gz')
assert hashlib.sha256(p.read_bytes()).hexdigest() == '5f7d44436735100afe708ecc31efdaca7372d736fe3ffed4a08814c2ee8777b5'
with tarfile.open(p) as archive:
    archive.extractall('.', filter='data')
PYCODE
python scripts/verify.py --bundles
```
The final check also requires the benchmark data bundle described in the artifact README. Refer to that README for pinned dependencies and commands to rerun conformal evaluation. Full retraining is a separate reproduction level and can differ across platforms.

## Scope and attribution

This repository contains research benchmark model outputs only. It contains no manuscript, LaTeX source, review reports or audit reports. Dataset sources and their terms are documented in the artifact's DATA.md and THIRD_PARTY.md. No new license over upstream datasets is implied.

	---
	language: en
	pretty_name: Score Diffusion Cached Predictions
	size_categories:
	- 100K<n<1M
	tags:
	- graph-learning
	- conformal-prediction
	- logits
	---
	# Score diffusion: cached classifier predictions

	Public prediction cache for When Does Score Diffusion Help Graph Conformal Prediction? by Xuan-Bach Le.

	These files contain logits, split indices and training metadata. They are not model weights or loadable trained-model checkpoints. The original training script did not save a state_dict. They support recalibration and evaluation on the stored graphs, but cannot run inference on new graphs.

	Code, data, supplementary PDF and reproduction instructions: https://github.com/lexuanbach/score-diffusion-artifact

	## Contents

	`predictions.tar.gz` contains 330 `.pt` files under `results/preds/`: 13 benchmark graphs × 5 classifiers (GCN, GAT, GraphSAGE, GCNII, MLP) × 5 seeds, plus 5 GCN temporal-split caches for ogbn-arxiv. Each dictionary has `logits`, `train_idx`, `val_idx`, and `info`. `info` identifies the dataset, model, seed, split and training summary.

	Archive SHA-256: `5f7d44436735100afe708ecc31efdaca7372d736fe3ffed4a08814c2ee8777b5`.
	`predictions-files.json` records the size and SHA-256 of every extracted file. This is the same archive published in GitHub release v1.0.0.

	## Download into the artifact

	From the cloned artifact directory:

	```bash
	curl -fL https://huggingface.co/datasets/lexuanbach/score-diffusion-predictions/resolve/main/predictions.tar.gz -o predictions.tar.gz
	python - <<'PYCODE'
	import hashlib, tarfile
	from pathlib import Path
	p = Path('predictions.tar.gz')
	assert hashlib.sha256(p.read_bytes()).hexdigest() == '5f7d44436735100afe708ecc31efdaca7372d736fe3ffed4a08814c2ee8777b5'
	with tarfile.open(p) as archive:
	archive.extractall('.', filter='data')
	PYCODE
	python scripts/verify.py --bundles
	```

	The final check also requires the benchmark data bundle described in the artifact README. Refer to that README for pinned dependencies and commands to rerun conformal evaluation. Full retraining is a separate reproduction level and can differ across platforms.

	## Scope and attribution

	This repository contains research benchmark model outputs only. It contains no manuscript, LaTeX source, review reports or audit reports. Dataset sources and their terms are documented in the artifact's DATA.md and THIRD_PARTY.md. No new license over upstream datasets is implied.