# TrustAGI-Lab/Awesome-Graph-Neural-Networks

Paper Lists for Graph Neural Networks

Repository: https://github.com/TrustAGI-Lab/Awesome-Graph-Neural-Networks
Canonical: https://ross.abutalabs.com/products/awesome-graph-neural-networks
License Family: other
Topics: graph-network, convolutional-networks, deep-learning, graph-attention, generated-graphs, graph-auto-encoder
Last push: 2023-12-29T15:02:47+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": 2802, "days_push": 978, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2310, forks 377 (observed 2026-08-28T04:06:36.004400+00:00)

## What it is
A curated awesome-list of research papers and resources on Graph Neural Networks, organized by model family (recurrent, convolutional, autoencoder, spatial-temporal) and application domain. It is a reading/reference resource, not runnable software.

## Use cases
- find papers on graph neural networks
- learn about graph convolutional networks
- survey of graph attention networks
- reading list for graph autoencoders
- find GNN applications in chemistry or healthcare
- get started with graph neural network research

## When to choose
- you want a curated, categorized bibliography of GNN research
- you are surveying GNN methods and applications before implementing one

## When to avoid
- you need a runnable GNN library or framework
- you need tutorials with code rather than paper links

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning
- domain: deep-learning, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: graph-neural-networks, paper-list, awesome-list, graph-attention, graph-autoencoder, research-papers

## Member repositories
- TrustAGI-Lab/Awesome-Graph-Neural-Networks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:36.004400+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 2026-08-30T02:39:33.376608+00:00, confidence not recorded.
  - readme: https://github.com/TrustAGI-Lab/Awesome-Graph-Neural-Networks (fetched 2026-08-28T04:06:36.004400+00:00, sha 96272af9a7a7)
- Data as of 2026-08-30T08:39:29.467469+00:00.