tkipf/gae

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1742 stars · 353 forks observed · 2026-08-28

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

A TensorFlow implementation of (Variational) Graph Auto-Encoders from Kipf & Welling's 2016 paper, supporting unsupervised learning, clustering, and link prediction on graphs. It provides GCN-based encoder models (gcn_ae and gcn_vae) with example training on citation network datasets like Cora, Citeseer, and Pubmed.

Use cases

• predict missing links in a graph network
• run unsupervised learning on graph-structured data
• cluster nodes in a citation or relational network
• reproduce the variational graph auto-encoder paper results
• experiment with graph convolutional network encoders
• do matrix completion or recommendation with graph side information

When to choose

• you need a reference implementation of GAE/VGAE for research or reproduction
• you want to do link prediction on graphs with TensorFlow 1.x
• you're working with citation network datasets like Cora or Citeseer

When to avoid

• you need PyTorch or modern TensorFlow 2.x support
• you want a production-ready, actively maintained library
• you need scalable training on very large graphs out of the box

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python graph-autoencoder graph-convolutional-networks link-prediction tensorflow unsupervised-learning research-code algorithms

Member repositories

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markdown · JSON · MCP: product_card(name="tkipf/gae")