benedekrozemberczki/CapsGNN

Health v2 · maintenance only

Adoption not part of the score

1278 stars · 197 forks observed · 2026-08-28

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

A PyTorch implementation of the Capsule Graph Neural Network (CapsGNN) from ICLR 2019, which uses capsule networks and routing mechanisms to generate graph embeddings for graph classification. It is research code accompanying the published paper.

Use cases

• classify graphs with a capsule graph neural network
• reproduce CapsGNN results from the ICLR 2019 paper
• generate graph-level embeddings from node features
• benchmark graph classification models on standard datasets
• study capsule networks applied to graph-structured data

When to choose

• you need a reference implementation of CapsGNN for research or reproduction
• you want to experiment with capsule-based graph classification in PyTorch

When to avoid

• you need a production-ready or actively maintained GNN framework
• you want broad model support beyond the CapsGNN architecture
• you need compatibility with recent PyTorch versions without adaptation

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning graph-processing python graph-neural-network capsule-network graph-classification pytorch iclr-2019 research-code

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