# Cora

**Task**: Paper topic classification (citation network)  
**Size band**: small · **Label type**: semantic  
**Label column**: `subject` · **Converter**: `converters/convert_linqs.py`

Classic citation network of machine-learning papers; the label is one of 7 subject areas and features are 1,433 binary bag-of-words indicators. Strongly homophilous — useful as a calibration baseline against the GNN literature.

## Converted graphs (neext/)

| graph | nodes | edges | classes | feature cols | isolated | class counts |
|---|---|---|---|---|---|---|
| default | 2,708 | 5,278 | 7 | 1433 | 0 | Neural_Networks: 818, Probabilistic_Methods: 426, Genetic_Algorithms: 418, Theory: 351, Case_Based: 298, Reinforcement_Learning: 217, … |

*Conversion notes*: LINQS .content/.cites; 1433 binary bag-of-words feature columns; 0 citation edges dropped (endpoint not in .content); source paper IDs remapped (id_mapping.csv).

## Source

- [cora.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz) — 168,052 bytes, sha256 `0d4ed463d1627bb7…`, fetched 2026-07-23

**License**: LINQS research distribution  
**Citation**: Sen, Namata, Bilgic, Getoor, Gallagher, Eliassi-Rad. Collective Classification in Network Data. AI Magazine 2008.

- https://linqs.org/datasets/
- https://github.com/kimiyoung/planetoid

## Caveats

- Homophily-driven labels favor message-passing GNNs; egonet embeddings are expected to trail here.

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*Generated by converters/make_cards.py; stats from metadata.json. Raw files: `source/`. NEExT tables: `neext/`.*