Weibo (PyGOD)
Task: Social spam detection
Size band: medium · Label type: anomaly
Label column: is_outlier · Converter: converters/convert_pygod.py
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).
Converted graphs (neext/)
| graph | nodes | edges | classes | feature cols | isolated | class counts |
|---|---|---|---|---|---|---|
| default | 8,405 | 377,271 | 2 | 400 | 0 | 0: 8,058, 1: 347 |
Conversion notes: PyGOD .pt (torch pickle, trusted pygod-team/data source) via stub unpickling; edge_index symmetrized; 400 x_* feature columns.
Source
- weibo.pt.zip — 13,339,024 bytes, sha256
c4a5fe4ca61a9566…, fetched 2026-07-23
License: MIT (pygod-team/data)
Citation: Liu et al. BOND. NeurIPS 2022 D&B (data: Zhao et al.).
Caveats
- Dense — k_hop=1 with sampling first.
- Torch-pickle source; stub-unpickled from the hash-verified official repo.
Generated by converters/make_cards.py; stats from metadata.json. Raw files: source/. NEExT tables: neext/.