Learning Graph Neural Networks with DGL -- The WebConf 2020 Tutorial
Watch a video tutorial presented by AWS deep learning scientists and engineers at The Web Conference 2020.
What is new in DGL v0.4.3 release?
The DGL v0.4.3 release brings many new features for an enhanced usability and system efficiency. The article takes a peek at some of the major highlights.
DGL v0.4 Release (heterogeneous graph update)
We are thrilled to announce the 0.4 release! This includes support of heterogeneous graphs, release of a package for training knowledge graph embedding and several feature updates and bug fixes....
DGL v0.3.1 Release
We have received many requests from our community for more GNN layers, models and examples. This is the time to respond. In this minor release, we enriched DGL with a...
Framework Agnostic
Build your models with PyTorch, TensorFlow or MXNet.
High Performant
DGL adopts advanced optimization techniques like kernel fusion, multi-thread and multi-process acceleration, and automatic sparse format tuning. Compared with other popular GNN frameworks such as PyTorch Geometric, DGL is both faster and more memory-friendly.
Ecosystem of Domain specific toolkits
DGL supports a variety of domains. DGL-KE is an easy-to-use and highly scalable package for learning large-scale knowledge graph embeddings. DGL-LifeSci is a specialized package for applications in bioinformatics and cheminformatics powered by graph neural networks.
News
Latest Updates
Keep track of what's new in DGL, such as important bug fixes, new features, new releases, etc.
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Blogs
Visit Blogs
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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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.