Blog
DGL 2.1: GPU Acceleration for Your GNN Data Pipeline
By Muhammed Fatih Balin, in release
The DGL 2.1 introduces GPU acceleration for the whole GNN data loading pipeline in GraphBolt, including the graph sampling and feature fetching stages.
Read more6 March
DGL 2.0: Streamlining Your GNN Data Pipeline from Bottleneck to Boost
The arrival of DGL 2.0 marks a significant milestone in the field of GNNs, offering substantial improvements in data loading capabilities.
Read more26 January
DGL 1.0: Empowering Graph Machine Learning for Everyone
We are thrilled to announce the arrival of DGL 1.0, a significant milestone of the past 3+ years of development.
Read more20 February
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.
Read more28 November