# Node Classification Eval

> This evaluation probes a model's ability to perform node classification on graphs across a spectrum of homophily regimes, from strongly heterophilic to strongly homophilic. It specifically tests the framework's adaptive capability to switch between a combinatorial predictor and a neural refinement stage based on validation performance. Use when the user wants to benchmark on Texas, Cornell, Actor, CiteSeer, Cora, Pubmed, or asks about evaluating this task. Reports classification accuracy.

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

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# node-classification-eval

> Interpretable and Adaptive Node Classification on Heterophilic Graphs via Combinatorial Scoring and Hybrid Learning — Vahidi (2025) (arXiv:2512.22221, 2025)

## What this evaluates

This evaluation probes a model's ability to perform node classification on graphs across a spectrum of homophily regimes, from strongly heterophilic to strongly homophilic. It specifically tests the framework's adaptive capability to switch between a combinatorial predictor and a neural refinement stage based on validation performance.

## Datasets

- **Texas** — total 183; splits: train (-1), val (-1), test (-1); repo https://github.com/AhmedBegggaUA/Diffusion-Jump-GNNs/tree/main/splits
- **Cornell** — total 183; splits: train (-1), val (-1), test (-1); repo https://github.com/AhmedBegggaUA/Diffusion-Jump-GNNs/tree/main/splits
- **Actor** — total 7600; splits: train (-1), val (-1), test (-1); repo https://github.com/AhmedBegggaUA/Diffusion-Jump-GNNs/tree/main/splits
- **CiteSeer** — total 3327; splits: train (-1), val (-1), test (-1); repo https://github.com/AhmedBegggaUA/Diffusion-Jump-GNNs/tree/main/splits
- **Cora** — total 2708; splits: train (-1), val (-1), test (-1); repo https://github.com/AhmedBegggaUA/Diffusion-Jump-GNNs/tree/main/splits
- **Pubmed** — total 19717; splits: train (-1), val (-1), test (-1); repo https://github.com/AhmedBegggaUA/Diffusion-Jump-GNNs/tree/main/splits

## Metrics

- `classification accuracy` **(primary)** — range: percent
  - Fraction of correctly predicted node labels out of the total number of nodes in the test set. Results are averaged across 10 predefined data splits (48% train / 32% val / 20% test), with standard deviation reported.

## Input / output format

**Input**: Graph adjacency structure, node feature vectors, and node labels (for training/validation). The model processes the graph structure and features to predict class labels for each node.

**Output**: A predicted class label for each node in the graph.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return (correct / len(gold_labels)) * 100

# Average over 10 fixed splits
split_accuracies = [compute_accuracy(preds[i], gold[i]) for i in range(10)]
mean_accuracy = sum(split_accuracies) / 10
std_accuracy = (sum((x - mean_accuracy)**2 for x in split_accuracies) / 10) ** 0.5
```

## Common pitfalls

- Using random or standard 80/10/10 splits instead of the 10 fixed predefined splits (48/32/20) explicitly required for comparability.
- Tuning hyperparameters on validation or test nodes, which violates the protocol's requirement for training-only cross-validation/random search.
- Applying the neural refinement stage unconditionally; the protocol mandates a validation-gated selection where refinement is only used if it improves validation accuracy by a fixed margin.

## Evidence (verbatim from paper)

> All results are reported as classification accuracy averaged over the 10 splits, together with the corresponding standard deviation. For each split, model selection and all validation-based decisions are performed exclusively on the validation set, and final accuracy is reported only on the held-out test set.

## Citation

```bibtex
@misc{vahidi2025interpretable,
  title={Interpretable and Adaptive Node Classification on Heterophilic Graphs via Combinatorial Scoring and Hybrid Learning},
  author={Vahidi (2025)},
  year={2025},
  note={arXiv:2512.22221}
}
```

- arXiv: 2512.22221