LirongWu/awesome-graph-self-supervised-learning resource

Health v2 · maintenance only

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Adoption not part of the score

1436 stars · 166 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated awesome-list of papers, code, and datasets for self-supervised learning on graphs, accompanying a TKDE survey paper. It organizes methods into contrastive, generative, and predictive categories with summaries of implementations and common graph datasets.

Use cases

• find papers on self-supervised learning for graphs
• compare contrastive vs generative graph pre-training methods
• find open-source implementations of graph SSL methods
• find common graph datasets for representation learning benchmarks
• learn about graph neural network pre-training techniques
• get started with unsupervised learning on graph-structured data

When to choose

• you need a survey-style entry point into graph self-supervised learning research
• you want a categorized reading list with linked code and datasets
• you are writing a literature review on graph representation learning

When to avoid

• you need a production-ready library or framework to train models
• you want maintained, runnable code rather than paper links
• you need non-graph self-supervised learning resources

Facets

learning-resource · maturity active

machine-learning deep-learning machine-learning deep-learning tutorials python awesome-list graph-neural-networks self-supervised-learning representation-learning graph-contrastive-learning pre-training survey

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