# Wiki Alumni Eval

> Evaluates knowledge graph models on node and entity classification under edge incompleteness, and link prediction with varying feature/class configurations. It probes how well models leverage relational structure, node features, and joint link prediction capacity to handle missing edges and predict missing links. Use when the user wants to benchmark on WikiAlumni, or asks about evaluating this task. Reports validation accuracy.

- Skill: `qhjqhj00/wiki-alumni-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/wiki-alumni-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/wiki-alumni-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/wiki-alumni-eval

---


# wiki-alumni-eval

> Node Classification Meets Link Prediction on Knowledge Graphs — Abboud et al. (2021) (arXiv:2106.07297, 2021)

## What this evaluates

Evaluates knowledge graph models on node and entity classification under edge incompleteness, and link prediction with varying feature/class configurations. It probes how well models leverage relational structure, node features, and joint link prediction capacity to handle missing edges and predict missing links.

## Datasets

- **WikiAlumni** — total ?; splits: 100% (-1), 90% (-1), 80% (-1)

## Metrics

- `validation accuracy` **(primary)** — range: [0, 1]
  - Fraction of correctly predicted node or entity classes out of the total instances in the validation set.
- `mean rank (MR)` — range: [1, N]
  - Average rank of the true tail entity across all test triples, ranked by model score.
- `mean reciprocal rank (MRR)` — range: [0, 1]
  - Average of the reciprocal of the rank of the true tail entity for each test triple.
- `Hits@10 (H@10)` — range: [0, 1]
  - Fraction of test triples where the true tail entity appears in the top 10 ranked predictions.

## Input / output format

**Input**: Knowledge graph instances containing entity node features, edge structure (relations), and optional class labels. Models are evaluated in configurations with or without node features and node classes.

**Output**: For classification: a predicted class label. For link prediction: a continuous score for each candidate entity used to generate a ranked list.

## Scoring recipe

```python
def compute_metrics(predictions, golds):
    # Classification
    accuracy = sum(p == g for p, g in zip(predictions, golds)) / len(golds)
    
    # Link Prediction
    ranks = [rank_of_true_entity(entity_scores) for entity_scores in predictions]
    mr = sum(ranks) / len(ranks)
    mrr = sum(1.0 / r for r in ranks) / len(ranks)
    hits10 = sum(1 if r <= 10 else 0 for r in ranks) / len(ranks)
    return accuracy, mr, mrr, hits10
```

## Common pitfalls

- Confusing 'node classification' (uses node features + edges) with 'entity classification' (uses only edges/embeddings, explicitly discards features).
- Assuming GNN baselines and MLP-X models are directly comparable without noting MLP-X's joint link prediction capacity versus GNNs' distinct link prediction setup.
- Misinterpreting BoxE scoring direction: lower scores indicate higher confidence, contrary to most embedding models.

## Evidence (verbatim from paper)

> Finally, we evaluate using standard metrics, namely mean rank (MR), mean reciprocal rank (MRR), and Hits@10 (H@10) [[3]].

## Citation

```bibtex
@misc{abboud2021nodeclassification,
  title={Node Classification Meets Link Prediction on Knowledge Graphs},
  author={Abboud et al. (2021)},
  year={2021},
  note={arXiv:2106.07297}
}
```

- arXiv: 2106.07297