Framework Agnostic
Build your models with PyTorch, TensorFlow or Apache MXNet.
Efficient and Scalable
Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure.
Diverse Ecosystem
DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others.
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DGL 1.0: Empowering Graph Machine Learning for Everyone
We are thrilled to announce the arrival of DGL 1.0, a significant milestone of the past 3+ years of development.
Improving Graph Neural Networks via Network-in-network Architecture
As Graph Neural Networks (GNNs) has become increasingly popular, there is a wide interest of designing deeper GNN architecture. However, deep GNNs suffer from the oversmoothing issue where the learnt...
Accelerating Partitioning of Billion-scale Graphs with DGL v0.9.1
Check out how DGL v0.9.1 helps users partition graphs of billions of nodes and edges.
v0.9 Release Highlights
Check out the highlighted features of the new 0.9 release!
Blogs
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Deep learning on graphs is very new direction. We use blogs to introduce new ideas and researches of this area and explains how DGL can support them very easily.
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Discussion
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Got questions? Interested in contributing? or simply want to know what others are playing with? Use our forum for all kinds of discussion.
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Brought to you by NYU, NYU-Shanghai, and Amazon AWS.
By far the cleanest and most elegant library for graph neural networks in PyTorch. Highly recommended! Unifies Capsule Nets (GNNs on bipartite graphs) and Transformers (GCNs with attention on fully-connected graphs) in a single API.
I taught my students Deep Graph Library (DGL) in my lecture on "Graph Neural Networks" today. It is a great resource to develop GNNs with PyTorch.
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