# graphdeeplearning/benchmarking-gnns

Repository for benchmarking graph neural networks (JMLR 2023)

Repository: https://github.com/graphdeeplearning/benchmarking-gnns
Canonical: https://ross.abutalabs.com/products/benchmarking-gnns
Homepage: https://arxiv.org/abs/2003.00982
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: graph-representation-learning, graph-neural-networks, benchmark-framework, graph-deep-learning, pytorch, dgl, deep-learning
Last push: 2023-06-22T04:03:53+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2374, "days_push": 1168, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2671, forks 459 (observed 2026-08-28T04:07:09.526318+00:00)

## What it is
An open-source benchmarking framework for graph neural networks (GNNs) published as a JMLR 2023 paper. It provides reproducible pipelines, diverse mathematical and real-world graph datasets (ZINC, AQSOL, PATTERN, CLUSTER, etc.), and a common parameter budget for fair comparison of GNN architectures.

## Use cases
- benchmark graph neural network architectures fairly
- reproduce published GNN results on standard datasets
- compare message-passing GNNs under the same parameter budget
- evaluate new GNN designs on molecular graph regression
- test GNNs on theoretical graph properties like cycle counting
- add a custom dataset or GNN architecture to a benchmark suite

## When to choose
- you need standardized, reproducible comparisons of GNN models
- you are doing GNN research and want established baselines and leaderboards
- you want curated graph datasets like ZINC, AQSOL, PATTERN, or CLUSTER with ready pipelines

## When to avoid
- you need a production GNN library for deploying models rather than benchmarking
- you work outside PyTorch/DGL ecosystems
- you need actively updated support for the latest GNN architectures, as the repo's last release was 2023

## Facets
- artifact type: framework
- maturity: maintenance
- function: benchmarking, machine-learning, deep-learning, testing
- domain: machine-learning, deep-learning, developer-tools
- platform: python, cross-platform
- tags: graph-neural-networks, gnn, pytorch, dgl, benchmark-suite, graph-representation-learning, reproducibility, jupyter-notebook, algorithms, research, gpu, linux

## Member repositories
- graphdeeplearning/benchmarking-gnns (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:09.526318+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 2026-08-30T02:18:08.166933+00:00, confidence not recorded.
  - readme: https://github.com/graphdeeplearning/benchmarking-gnns (fetched 2026-08-28T04:07:09.526318+00:00, sha 2bca23e7173e)
  - homepage: https://arxiv.org/abs/2003.00982 (fetched 2026-08-29T10:00:49.182966+00:00, sha 4928b2cffebd)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T10:00:49.192331+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T10:00:49.196099+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T10:00:49.198159+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T10:00:49.194242+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.