# snap-stanford/ogb

Benchmark datasets, data loaders, and evaluators for graph machine learning

Repository: https://github.com/snap-stanford/ogb
Canonical: https://ross.abutalabs.com/products/ogb
Homepage: https://ogb.stanford.edu
Language: Python
License: MIT
License Family: permissive
Topics: graph-machine-learning, graph-neural-networks, deep-learning, datasets
Last push: 2025-05-06T07:32:11+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 20, release rhythm 8, longevity 100
- inputs: {"age_days": 2476, "days_push": 484, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2094, forks 406 (observed 2026-08-28T04:06:13.100515+00:00)

## What it is
The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning, covering node-, link-, and graph-level prediction tasks across diverse domains and scales. It integrates with PyTorch Geometric and DGL, providing automatic downloading, standardized splits, and unified evaluation with leaderboards.

## Use cases
- benchmark graph neural network models on standard datasets
- download and preprocess graph datasets for node classification
- evaluate link prediction models with standardized splits
- train GNNs on large-scale graphs like MAG240M
- predict molecular properties from molecular graphs
- compare my model against the state-of-the-art graph ML leaderboard
- load knowledge graph datasets for triple imputation

## When to choose
- you need realistic, standardized graph ML benchmarks with unified evaluation
- you want datasets compatible with PyTorch Geometric or DGL out of the box
- you need datasets spanning small to web-scale graphs across many domains
- you want to submit results to community leaderboards

## When to avoid
- you need a general-purpose graph processing library rather than benchmarks
- your task requires custom datasets not covered by OGB's collection
- you work outside the PyTorch/DGL ecosystem and cannot use the loaders

## Facets
- artifact type: dataset
- maturity: stable
- function: machine-learning, deep-learning, benchmarking, data-science
- domain: machine-learning, deep-learning, data-science
- platform: python, cross-platform
- tags: graph-neural-networks, graph-machine-learning, benchmark-datasets, pytorch-geometric, dgl, leaderboards, data-loaders, algorithms

## Member repositories
- snap-stanford/ogb (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:13.100515+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:55:26.629930+00:00, confidence not recorded.
  - readme: https://github.com/snap-stanford/ogb (fetched 2026-08-28T04:06:13.100515+00:00, sha 37918c8a6c21)
  - homepage: https://ogb.stanford.edu (fetched 2026-08-29T10:35:13.072625+00:00, sha 2c37b63e2dc1)
  - site_page: https://ogb.stanford.edu/docs/home (fetched 2026-08-29T10:35:13.081736+00:00, sha 9d55f2cf0560)
  - site_page: https://ogb.stanford.edu/docs/update (fetched 2026-08-29T10:35:13.083854+00:00, sha ea98f3c16f9e)
  - site_page: https://ogb.stanford.edu/docs/lsc (fetched 2026-08-29T10:35:13.085809+00:00, sha 245cd5495c33)
  - site_page: https://ogb.stanford.edu/docs/lsc/mag240m (fetched 2026-08-29T10:35:13.088221+00:00, sha aa445c75f0ac)
  - site_page: https://ogb.stanford.edu/docs/lsc/wikikg90mv2 (fetched 2026-08-29T10:35:13.090129+00:00, sha fa73064172e8)
  - site_page: https://ogb.stanford.edu/docs/lsc/pcqm4mv2 (fetched 2026-08-29T10:35:13.092245+00:00, sha a704f04d8285)
  - site_page: https://ogb.stanford.edu/docs/lsc/rules (fetched 2026-08-29T10:35:13.094118+00:00, sha a709fcac4b11)
  - site_page: https://ogb.stanford.edu/docs/lsc/leaderboards (fetched 2026-08-29T10:35:13.095783+00:00, sha 4d30e3e472ce)
- Data as of 2026-08-30T08:39:29.467469+00:00.