We are thrilled to announce the arrival of DGL 1.0, a cutting-edge machine learning framework for deep learning on graphs. 

In the latest DGL v0.9.1, we released a new pipeline for preprocess, partition and dispatch graph of billions of nodes or edges for distributed GNN training. At its core is a new data format called Chunked Graph Data Format (CGDF) which stores graph data by chunks. The new pipeline processes data chunks in parallel which not only reduces the memory requirement of each machine but also significantly accelerates the entire procedure. 

In the latest issue of Nature Machine Intelligence, Yousun Jung and his research team at Korea Institute of Science and Technology (KAIST) have proposed a novel generalized-template-based graph neural network called LocalTransform for accurate organic reactivity prediction . The generalized reaction templates demonstrate the built-in interpretability in addition to state-of-the-art prediction accuracy. LocalTransform has been implemented with DGL, DGL-LifeSci and PyTorch. 

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Six years after the first Graph Convolutional Networks paper, researchers are actively investigating more advanced GNN architecture or training methodology. As the developer team of DGL, we closely watch those new research trends and release features to facilitate them. Here, we're excited to announce a new major release DGL v0.9.

The new 0.8.2 update brings new additions for the GNN community.

V0.8.1 is a minor release that includes module updates, optimizations, new features and bug fixes.

We are excited to announce the release of DGL v0.8, which brings many new features as well as improvement on system performance.

Today, the GAMMA Lab of Beijing University of Posts and Telecommunications officially released OpenHGNN-0.1.0, an Open Source Toolkit for Heterogeneous Graph Neural Network. OpenHGNN is based on DGL [Deep Graph Library] and PyTorch, and includes SOTA models of heterogeneous graph.

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