Blog - page 5
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.
Read more1 April
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.
Read more8 October
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 ton of common GNN modules. We have also verified their correctness on some popular datasets so feel free to try them out. Another direction we are working on is to build more domain friendly packages based on DGL. As a first step, we released several pretrained GNN models for molecular property prediction and molecule generation (currently grouped under dgl.model_zoo namespace). We will continue explore this idea and release more domain specific models and packages.
Read more28 August
Large-Scale Training of Graph Neural Networks
By Da Zheng, Chao Ma, Ziyue Huang, Quan Gan, Yu Gai, Zheng Zhang, in blog
Many graph applications deal with giant scale. Social networks, recommendation and knowledge graphs have nodes and edges in the order of hundreds of millions or even billions of nodes. For example, a recent snapshot of the friendship network of Facebook contains 800 million nodes and over 100 billion links.
Read more13 June