I taught my students Deep Graph Library (DGL) in my lecture on "Graph Neural Networks" today. It is a great resource to develop GNNs with PyTorch.

A Deep Learning container (MXNet 1.6 and PyTorch 1.3) bundles all the software dependencies and the SageMaker API automatically sets up and scales the infrastructure required to train graphs.

Library for deep learning on graphs Why DGL? In the last few years, deep learning has enjoyed plenty of extraordinary successes. Many challenging tasks have been solved or close to being solved by Deep …

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, most GNN operations are memory-bound and require a significant amount of RAM. To …

Feb 20, 2023 · Library for deep learning on graphs GCN in DGL Sparse Graph Diffusion-based GNNs. Graph diffusion is a process of propagating or smoothing node features/signals along edges. Many …

Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (currently supporting PyTorch, MXNet and TensorFlow).

Jan 26, 2024 · The dgl.graphbolt doesn’t just give you flexibility, it also provides top performance under the hood. It features a compact graph data structure for efficient sampling, blazing-fast multi …

Mar 1, 2022 · New functions to create, transform and augment graph datasets, making it easier to conduct research on graph contrastive learning or repurposing a graph for different tasks.

Mar 6, 2024 · DGL 2.1: GPU Acceleration for Your GNN Data Pipeline We are happy to announce the release of DGL 2.1. In this release, we are making GNN data loading lightning fast. We introduce …

Sep 19, 2022 · A graph dataset typically consists of graph structure and the features associated with nodes/edges. If the graph is heterogeneous (i.e., having multiple types of nodes or edges), different …