weihua916/powerful-gnns

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1285 stars · 227 forks observed · 2026-08-28

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

Official PyTorch implementation of the experiments from the ICLR 2019 paper 'How Powerful are Graph Neural Networks?', introducing the Graph Isomorphism Network (GIN). It provides training and evaluation code for GIN on graph classification benchmarks using the paper's cross-validation protocol.

Use cases

• implement a graph isomorphism network in pytorch
• reproduce the GIN paper experiments
• run graph neural network graph classification on benchmark datasets
• study the reference code behind a foundational GNN theory paper
• get GIN baseline code for GNN research

When to choose

• You need the exact reference implementation of GIN to cite or reproduce the ICLR 2019 results
• You want to understand the algorithm from 'How Powerful are Graph Neural Networks?' by reading its official code
• You need a GIN baseline for graph classification experiments

When to avoid

• You need a maintained, general-purpose GNN framework with many models and active support
• You require compatibility with recent PyTorch versions, since the code was tested on PyTorch 0.4.1 and 1.0.0
• You want a plug-and-play library rather than research experiment code with manual hyperparameter tuning

Facets

library · maturity maintenance

deep-learning machine-learning deep-learning machine-learning graph-processing python graph-neural-networks gin graph-isomorphism-network pytorch graph-classification paper-implementation iclr-2019 research-code

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