# ChandlerBang/awesome-self-supervised-gnn

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

Repository: https://github.com/ChandlerBang/awesome-self-supervised-gnn
Canonical: https://ross.abutalabs.com/products/awesome-self-supervised-gnn
Language: Python
License Family: other
Topics: graph-neural-networks, pretraining, self-supervised-learning, deep-learning, machine-learning, graph-mining, pre-training, graph-self-supervised-learning
Last push: 2024-02-02T14:48:59+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2290, "days_push": 943, "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 1728, forks 167 (observed 2026-08-28T04:05:28.439532+00:00)

## What it is
A curated awesome-list of research papers on self-supervised learning and pretraining for Graph Neural Networks (GNNs), organized by publication year. It links papers to their venues, arXiv pages, and available code implementations.

## Use cases
- find papers on self-supervised graph neural networks
- research pretraining methods for GNNs
- find code implementations of graph contrastive learning papers
- survey the state of the art in graph self-supervised learning
- prepare a literature review on graph representation learning
- discover recent graph contrastive learning papers by year

## When to choose
- you need a curated, categorized reading list on self-supervised GNN research
- you want paper-to-code links for graph pretraining methods
- you are surveying graph contrastive learning literature across years

## When to avoid
- you need runnable software or a library rather than a paper list
- you need tutorials or courses rather than research papers
- you need maintained tooling with a license and releases

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, graph-processing, awesome-lists
- platform: python
- tags: awesome-list, graph-neural-networks, self-supervised-learning, pretraining, papers, research

## Member repositories
- ChandlerBang/awesome-self-supervised-gnn (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:28.439532+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 2026-08-30T03:31:57.915045+00:00, confidence not recorded.
  - readme: https://github.com/ChandlerBang/awesome-self-supervised-gnn (fetched 2026-08-28T04:05:28.439532+00:00, sha 501cce0f2964)
- Data as of 2026-08-30T08:39:29.467469+00:00.