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DGL 2.0: Streamlining Your GNN Data Pipeline from Bottleneck to Boost

By DGLTeam, in release

The arrival of DGL 2.0 marks a significant milestone in the field of GNNs, offering substantial improvements in data loading capabilities.

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DGL 1.0: Empowering Graph Machine Learning for Everyone

By DGLTeam, in release

We are thrilled to announce the arrival of DGL 1.0, a significant milestone of the past 3+ years of development.

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Improving Graph Neural Networks via Network-in-network Architecture

By Yakun Song, in blog

As Graph Neural Networks (GNNs) has become increasingly popular, there is a wide interest of designing deeper GNN architecture. However, deep GNNs suffer from the oversmoothing issue where the learnt node representations quickly become indistinguishable with more layers. This blog features a simple yet effective technique to build a deep GNN without the concern of oversmoothing. The new architecture, Network in Graph Neural Networks (NGNN) inspired by the network-in-network architecture for computer vision, has shown superior performance on multiple Open Graph Benchmark (OGB) leaderboards.

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Accelerating Partitioning of Billion-scale Graphs with DGL v0.9.1

By DGLTeam, in release

Check out how DGL v0.9.1 helps users partition graphs of billions of nodes and edges.

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