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DGL v0.4 Release (heterogeneous graph update)

By DGLTeam, in release

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.

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DGL v0.3.1 Release

By DGLTeam, in 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.

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Large-Scale Training of Graph Neural Networks

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.

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DGL v0.3 Release

By DGLTeam, in release

V0.3 release includes many crucial updates. (1) Fused message passing kernels that greatly boost the training speed of GNNs on large graphs. (2) Add components to enable distributed training of GNNs on giant graphs with graph sampling. (3) New models and NN modules. (4) Many other bugfixes and other enhancement.

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