Graph Neural Network has become the new fashion in many graph-based learning problems. Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (e.g. PyTorch, MXNet, Gluon etc.). As the team behind this library, we want to share with you the new release of DGL (v0.3) that is much faster (up to 19x faster) and more scalable for training GNNs on large graphs (up to 8x larger). Checkout our full release note here: https://www.dgl.ai/release/2019/06/12/release.html .

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For whom have never heard of DGL or Graph Neural Network, maybe it is worth to take a look at this new trend of geometric deep learning. Some links here:

• Checkout this 10-minute tutorial about how to use Graph Neural Network to predict community membership (https://docs.dgl.ai/tutorials/basics/1_first.html).
• Checkout more about how a variety of models can be unified under the message passing framework and can be implemented in DGL (https://docs.dgl.ai/tutorials/models/index.html).
• Our github repo: https://github.com/dmlc/dgl
• Our project site: https://www.dgl.ai/ . We publish many blogs about the new findings in this area.