README.md

---
pretty_name: NEExT Egonet Experiment Datasets
tags:
  - graph-machine-learning
  - node-classification
  - egonet
  - networks
viewer: false
---
# NEExT Egonet Experiment Datasets

Single-graph network datasets with per-node class labels, curated for egonet-based
node classification with [NEExT](https://github.com/AnomalyPoint/NEExT): each node's
k-hop egonet becomes a subgraph, subgraphs are embedded, and nodes are classified by
their egonet embeddings.

Every dataset folder contains:

- `card.md` — information card: task, stats, source, license, citation, caveats
- `metadata.json` — machine-readable: source URLs, sha256 checksums, per-graph stats, conversion details
- `neext/` — NEExT-ready tables per graph: `edges.csv`/`nodes.csv` and Parquet mirrors
  (`edges.parquet`/`nodes.parquet`; for coauthor-cs, coauthor-physics and reddit-graphsage the
  Parquet nodes include full feature matrices omitted from CSV for size)
- `source/` — original upstream files, unmodified, hash-recorded

Table contract: `edges` (`src_node_id`,`dest_node_id`) + `nodes` (`node_id`, `<label>`, features...),
int64 node IDs, undirected deduplicated edges, fully-populated label column.

**Licensing**: each dataset keeps its upstream license/terms — see the per-dataset `card.md`.
This repository is a research mirror; cite the original authors listed in each card.

## Datasets
| dataset | task | band | graphs | nodes | edges | classes | label | license |
|---|---|---|---|---|---|---|---|---|
| [actor](actor/card.md) | Actor category classification | medium | 1 | 7,600 | 26,659 | 5 | `actor_class` | Not stated (geom-gcn repo) |
| [airports](airports/card.md) | Airport activity-level classification | small | 3 | 1,720 | 20,595 | 4 | `activity_quartile` | MIT (struc2vec repository) |
| [amazon-computers](amazon-computers/card.md) | Product category classification (co-purchase) | medium | 1 | 13,752 | 245,861 | 10 | `category` | MIT (shchur/gnn-benchmark packaging) |
| [amazon-photo](amazon-photo/card.md) | Product category classification (co-purchase) | medium | 1 | 7,650 | 119,081 | 8 | `category` | MIT (shchur packaging) |
| [amazon-ratings](amazon-ratings/card.md) | Product rating-class prediction | medium | 1 | 24,492 | 93,050 | 5 | `rating_class` | MIT (yandex-research) |
| [bitcoin-otc](bitcoin-otc/card.md) | Fraudulent-user detection (derived labels) | small | 1 | 5,881 | 21,492 | 3 | `trust_label` | SNAP research use; cite |
| [blogcatalog](blogcatalog/card.md) | Blogger interest-group classification | medium | 1 | 7,460 | 131,034 | 38 | `group` | deepwalk repo GPL-3.0; data from ASU social computing repository |
| [books](books/card.md) | Outlier book detection (Amazon co-purchase) | small | 1 | 1,418 | 3,695 | 2 | `is_outlier` | MIT (pygod-team/data) |
| [citeseer](citeseer/card.md) | Paper topic classification (citation network) | small | 1 | 3,312 | 4,536 | 6 | `subject` | LINQS research distribution |
| [coauthor-cs](coauthor-cs/card.md) | Research-field classification (co-authorship) | medium | 1 | 18,333 | 81,894 | 15 | `field` | MIT (shchur packaging) |
| [coauthor-physics](coauthor-physics/card.md) | Research-field classification (co-authorship) | medium | 1 | 34,493 | 247,962 | 5 | `field` | MIT (shchur packaging) |
| [cora](cora/card.md) | Paper topic classification (citation network) | small | 1 | 2,708 | 5,278 | 7 | `subject` | LINQS research distribution |
| [deezer-europe](deezer-europe/card.md) | User gender classification | medium | 1 | 28,281 | 92,752 | 2 | `gender` | SNAP; cite FEATHER |
| [disney](disney/card.md) | Outlier movie detection (co-purchase) | small | 1 | 124 | 335 | 2 | `is_outlier` | MIT (pygod-team/data) |
| [email-eu-core](email-eu-core/card.md) | Department classification from email traffic | small | 1 | 1,005 | 16,064 | 42 | `department` | SNAP research use; cite the source papers |
| [enron](enron/card.md) | Email spam detection | medium | 1 | 13,533 | 176,987 | 2 | `is_outlier` | MIT (pygod-team/data) |
| [facebook-page-page](facebook-page-page/card.md) | Page category classification | medium | 1 | 22,470 | 170,823 | 4 | `page_type` | MUSAE repo GPL-3.0; cite MUSAE |
| [github-developers](github-developers/card.md) | Web vs ML developer classification | medium | 1 | 37,700 | 289,003 | 2 | `ml_developer` | SNAP / MUSAE; cite MUSAE |
| [lastfm-asia](lastfm-asia/card.md) | User country classification | medium | 1 | 7,624 | 27,806 | 18 | `country` | SNAP; cite FEATHER |
| [minesweeper](minesweeper/card.md) | Mine prediction on a synthetic grid | medium | 1 | 10,000 | 39,402 | 2 | `is_mine` | MIT (yandex-research) |
| [ogbn-arxiv](ogbn-arxiv/card.md) | arXiv subject-area classification | large | 1 | 169,343 | 1,157,799 | 40 | `arxiv_category` | ODC-BY |
| [polblogs](polblogs/card.md) | Political-leaning classification | small | 1 | 1,490 | 16,715 | 2 | `leaning` | Research use; cite Adamic & Glance |
| [pubmed](pubmed/card.md) | Paper topic classification (citation network) | medium | 1 | 19,717 | 44,324 | 3 | `diabetes_type` | LINQS research distribution |
| [questions](questions/card.md) | User churn prediction | medium | 1 | 48,921 | 153,540 | 2 | `is_active` | MIT (yandex-research) |
| [reddit-graphsage](reddit-graphsage/card.md) | Subreddit classification of posts | large | 1 | 232,965 | 11,606,919 | 41 | `subreddit` | SNAP research use; cite GraphSAGE |
| [reddit-pygod](reddit-pygod/card.md) | Banned-user detection | medium | 1 | 10,984 | 78,516 | 2 | `is_outlier` | MIT (pygod-team/data) |
| [roman-empire](roman-empire/card.md) | Syntactic-role classification | medium | 1 | 22,662 | 32,927 | 18 | `syntactic_role` | MIT (yandex-research) |
| [tolokers](tolokers/card.md) | Banned crowdworker prediction | medium | 1 | 11,758 | 519,000 | 2 | `is_banned` | MIT (yandex-research) |
| [twitch](twitch/card.md) | Explicit-content streamer classification | small-medium | 6 | 34,118 | 429,113 | 2 | `mature` | SNAP / MUSAE; GPL-3.0 code, cite MUSAE |
| [webkb](webkb/card.md) | University web-page classification | small | 3 | 617 | 1,006 | 5 | `page_class` | Not stated (geom-gcn repo) |
| [weibo](weibo/card.md) | Social spam detection | medium | 1 | 8,405 | 377,271 | 2 | `is_outlier` | MIT (pygod-team/data) |
| [wikics](wikics/card.md) | CS article branch classification | medium | 1 | 11,701 | 215,603 | 10 | `category` | MIT (dataset code); article text CC BY-SA |
| [wikipedia-articles](wikipedia-articles/card.md) | Traffic-level classification (binned) | small-medium | 3 | 19,109 | 400,497 | 5 | `traffic_quintile` | SNAP / MUSAE; cite MUSAE |