dmlc/dgl

Health v2 · maintenance only

Adoption not part of the score

14282 stars · 3054 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DGL (Deep Graph Library) is a Python package for deep learning on graph-structured data, built on top of existing frameworks like PyTorch, TensorFlow, and Apache MXNet. It provides efficient message-passing primitives, GNN layers, GPU-accelerated data pipelines, and distributed training for graphs with hundreds of millions of nodes and edges.

Use cases

• train graph neural networks on social network data
• predict molecular properties with GNNs
• build knowledge graph embeddings at scale
• run node classification on large graphs with mini-batch sampling
• implement graph transformer models
• benchmark GNN models on OGB datasets
• scale GNN training across multiple GPUs

When to choose

• you need a mature, GPU-ready library for graph neural networks
• you want framework-agnostic GNN support across PyTorch, TensorFlow, or MXNet
• you need to train on very large graphs with distributed or stochastic training
• you want ready-made GNN layers, model examples, and domain projects like DGL-KE or DGL-LifeSci

When to avoid

• your data is tabular or unstructured text with no graph structure
• you only need classical graph algorithms like shortest paths without deep learning
• you need a lightweight pure-PyTorch-native solution and prefer PyG's ecosystem
• you require a framework with no dependency on external DL backends

Facets

library · maturity stable

deep-learning machine-learning gpu-computing data-science deep-learning machine-learning graph-processing artificial-intelligence python windows graph-neural-networks gnn message-passing framework-agnostic pytorch tensorflow mxnet graph-sampling distributed-training linux macos gpu

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