# snap-stanford/GraphGym

Platform for designing and evaluating Graph Neural Networks (GNN)

Repository: https://github.com/snap-stanford/GraphGym
Canonical: https://ross.abutalabs.com/products/graphgym
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-11-10T05:37:18+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2149, "days_push": 1027, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1904, forks 199 (observed 2026-08-28T04:05:51.863855+00:00)

## What it is
GraphGym is a platform for designing and evaluating Graph Neural Networks (GNNs), built around a highly modularized pipeline covering data loading, model architecture, tasks, and evaluation. It enables reproducible experiments via configuration files and scalable parallel launching of thousands of GNN experiments with auto-generated analyses.

## Use cases
- design and evaluate graph neural networks
- run standardized GNN experiments reproducibly
- benchmark GNN architectures across node edge and graph tasks
- launch thousands of GNN experiments in parallel
- learn how GNNs work with a standardized implementation
- search the GNN design space for the best model
- register custom GNN layers and data loaders

## When to choose
- you need a modular, reproducible pipeline for GNN experiments
- you want to systematically compare many GNN architectures and configurations
- you are a researcher exploring the GNN design space
- you are a beginner wanting a standardized GNN implementation and evaluation setup

## When to avoid
- you need a general-purpose deep learning framework rather than a GNN-specific platform
- you need cutting-edge actively developed GNN features - consider the tightly integrated GraphGym+PyG version
- you work outside graph-structured data domains
- you need a no-code or GUI-based ML tool

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, benchmarking, data-science
- domain: machine-learning, deep-learning
- platform: python, windows
- tags: graph-neural-networks, gnn, pytorch, pytorch-geometric, experiment-management, graph-learning, neurips, algorithms, research, linux, macos

## Member repositories
- snap-stanford/GraphGym (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:51.863855+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 2026-08-30T03:11:51.891491+00:00, confidence not recorded.
  - readme: https://github.com/snap-stanford/GraphGym (fetched 2026-08-28T04:05:51.863855+00:00, sha 5bae4b54e94c)
  - registry_pypi: https://pypi.org/pypi/graphgym/json (fetched 2026-08-29T10:50:48.024715+00:00, sha 28a3dfc3f407)
- Data as of 2026-08-30T08:39:29.467469+00:00.