| # Weibo (PyGOD) |
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| **Task**: Social spam detection |
| **Size band**: medium · **Label type**: anomaly |
| **Label column**: `is_outlier` · **Converter**: `converters/convert_pygod.py` |
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| Sina Weibo user-user graph (shared-hashtag edges) with 400 post-derived features and organic spammer labels (10.3% anomalous). Real social misbehavior at Workbench-comfortable scale, but dense (avg degree ~90). |
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| ## Converted graphs (neext/) |
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| | graph | nodes | edges | classes | feature cols | isolated | class counts | |
| |---|---|---|---|---|---|---| |
| | default | 8,405 | 377,271 | 2 | 400 | 0 | 0: 8,058, 1: 347 | |
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| *Conversion notes*: PyGOD .pt (torch pickle, trusted pygod-team/data source) via stub unpickling; edge_index symmetrized; 400 x_* feature columns. |
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| ## Source |
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| - [weibo.pt.zip](https://raw.githubusercontent.com/pygod-team/data/main/weibo.pt.zip) — 13,339,024 bytes, sha256 `c4a5fe4ca61a9566…`, fetched 2026-07-23 |
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| **License**: MIT (pygod-team/data) |
| **Citation**: Liu et al. BOND. NeurIPS 2022 D&B (data: Zhao et al.). |
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| - https://github.com/pygod-team/data |
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| ## Caveats |
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| - Dense — k_hop=1 with sampling first. |
| - Torch-pickle source; stub-unpickled from the hash-verified official repo. |
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| --- |
| *Generated by converters/make_cards.py; stats from metadata.json. Raw files: `source/`. NEExT tables: `neext/`.* |
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