Aug 14, 2022 · Join us as we dive into GNNs for fraud detection and as we demonstrate how RAPIDS + DGL drastically reduces training time.
Aug 18, 2022 · RAPIDS cuGraph sampling algorithms execute 10x to 100x faster than similar CPU versions and scale to support massive size graphs. Join us as we.
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Sep 5, 2024 · Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow. KDD 2022: 4820-4821. [+][–]. 2010 – 2019. FAQ. see FAQ. What ...
Oct 28, 2024 · This post introduces an end-to-end AI workflow that uses graph neural networks (GNNs) offering a flexible, high-performance solution for fraud detection.
Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow (KDD 2022). Brad Rees, Xiaoyun Wang, Joe Eaton, Onur Yilmaz, Rick Ratzel ...
Aug 31, 2023 · In this post, I introduce how to use cuGraph-DGL, a GPU-accelerated library for graph computations. It extends Deep Graph Library (DGL), a popular framework ...
Accepted Lecture-Style Tutorials - KDD 2022 | Washington DC, U.S.
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Accelerated GNN training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow. Authors: Brad Rees (NVIDIA)*; Xiaoyun Wang (NVIDIA); Onur Yilmaz (NVIDIA); ...
KDD Tutorial on Accelerated GNN Training with DGL/PyG and cuGraph. 2021#. GTC ... Using RAPIDS AI to accelerate graph data science workflows . In 2020 ...
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May 11, 2023 · This blog covers how to use cuGraph to train a GNN with PyG, as well as how to convert an existing PyG workflow to one with cuGraph.
Oct 14, 2023 · Optimizing Fraud Detection in Financial Services with Graph Neural Networks and NVIDIA GPUs NVIDIA has partnered with DGL and PyG to add ...