| # Coauthor Physics |
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| **Task**: Research-field classification (co-authorship) |
| **Size band**: medium · **Label type**: semantic |
| **Label column**: `field` · **Converter**: `converters/convert_npz.py` |
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| Microsoft Academic co-authorship graph in physics; 5 field classes; the largest shchur benchmark (34.5k nodes). |
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| ## Converted graphs (neext/) |
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| | graph | nodes | edges | classes | feature cols | isolated | class counts | |
| |---|---|---|---|---|---|---| |
| | default | 34,493 | 247,962 | 5 | 0 | 0 | 2: 17,426, 0: 5,750, 1: 5,045, 4: 3,519, 3: 2,753 | |
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| *Conversion notes*: shchur gnn-benchmark npz; CSR adjacency symmetrized. Feature matrix too large for CSV (skipped; available in source npz). |
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| ## Source |
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| - [ms_academic_phy.npz](https://raw.githubusercontent.com/shchur/gnn-benchmark/master/data/npz/ms_academic_phy.npz) — 16,374,005 bytes, sha256 `e4d68468eba5fb8f…`, fetched 2026-07-23 |
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| **License**: MIT (shchur packaging) |
| **Citation**: Shchur et al. Pitfalls of GNN Evaluation. 2018. |
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| - https://github.com/shchur/gnn-benchmark |
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| ## Caveats |
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| - 8,415-dim features exceed the CSV budget — label-only nodes.csv; features remain in the source npz. |
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| --- |
| *Generated by converters/make_cards.py; stats from metadata.json. Raw files: `source/`. NEExT tables: `neext/`.* |
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