| # Cora |
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| **Task**: Paper topic classification (citation network) |
| **Size band**: small · **Label type**: semantic |
| **Label column**: `subject` · **Converter**: `converters/convert_linqs.py` |
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| 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. |
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| ## Converted graphs (neext/) |
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| | 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, … | |
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| *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). |
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| ## Source |
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| - [cora.tgz](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz) — 168,052 bytes, sha256 `0d4ed463d1627bb7…`, fetched 2026-07-23 |
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| **License**: LINQS research distribution |
| **Citation**: Sen, Namata, Bilgic, Getoor, Gallagher, Eliassi-Rad. Collective Classification in Network Data. AI Magazine 2008. |
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| - https://linqs.org/datasets/ |
| - https://github.com/kimiyoung/planetoid |
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| ## Caveats |
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| - 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/`.* |
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