THUDM/CogDL

Health v2 · maintenance only

Adoption not part of the score

1821 stars · 308 forks observed · 2026-08-28

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

CogDL is a Python/PyTorch toolkit for graph deep learning that lets researchers train and compare GNN and graph embedding models on tasks like node classification, graph classification, and link prediction. It provides optimized operators, hyper-parameter search, and a model leaderboard, published at WWW 2023.

Use cases

• train graph neural networks for node classification
• benchmark GNN models on graph datasets
• learn graph embeddings for link prediction
• run graph classification experiments with hyper-parameter search
• experiment with graph self-supervised learning like GraphMAE
• apply GNN models to custom graph datasets

When to choose

• you need a unified PyTorch framework for GNN research and baselines
• you want reproducible benchmarks across many graph models and datasets
• you need efficient, memory-optimized GNN training on GPU

When to avoid

• you need general deep learning beyond graphs
• you want a production graph ML service rather than a research toolkit
• you need a framework under very active development - releases have slowed

Facets

library · maturity maintenance

machine-learning deep-learning benchmarking data-science machine-learning deep-learning data-science python cross-platform graph-neural-networks gnn graph-embedding node-classification graph-classification link-prediction pytorch graph-representation-learning self-supervised-learning algorithms gpu

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markdown · JSON · MCP: product_card(name="THUDM/CogDL")