large-scale graph

DataFunSummit

Jan 14, 2023 · Artificial Intelligence

Deep Graph Library (DGL): Technical Features, Community Progress, and Challenges in Graph Deep Learning

This article provides a comprehensive overview of the Deep Graph Library (DGL), covering its technical characteristics, open‑source community developments, various graph learning tasks, message‑passing mechanisms, system design challenges, training strategies on single and multiple GPUs, inference optimization, and a Q&A comparing DGL with other frameworks.

AIDeep Graph LibraryGNN Training

0 likes · 15 min read

Deep Graph Library (DGL): Technical Features, Community Progress, and Challenges in Graph Deep Learning

Alimama Tech

Oct 12, 2022 · Artificial Intelligence

Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval

Decoupled Graph Neural Networks (DC‑GNN) improve large‑scale e-commerce ad recall by separating graph processing from CTR prediction, using multi‑task pretraining (edge prediction + contrastive learning), efficient deep linear aggregation, and a dual‑tower CTR model, achieving higher efficiency and performance on billions‑scale data.

CTR predictionDecoupled ArchitectureGraph Neural Networks

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Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval

DataFunSummit

Oct 1, 2022 · Artificial Intelligence

GraphLearn: An Industrial‑Scale Distributed Graph Learning Platform and Its System Optimizations

This article introduces GraphLearn, a large‑scale distributed graph learning platform designed for industrial GNN workloads, details its architecture, sampling implementation, training pipeline, system optimizations such as GPU‑accelerated sampling, and showcases real‑world applications in recommendation and risk control.

GPU accelerationGraph Neural NetworksRecommendation systems

0 likes · 13 min read

GraphLearn: An Industrial‑Scale Distributed Graph Learning Platform and Its System Optimizations

AntTech

Sep 28, 2021 · Databases

GeaGraph: Large-Scale Graph Computing System Wins World Internet Conference Award

The Ant Group and Tsinghua University’s jointly developed large‑scale graph computing system GeaGraph, recognized at the 2021 World Internet Conference, showcases world‑leading performance in trillion‑edge graph queries and exemplifies successful industry‑academia‑research collaboration for advanced database technology.

Big DataGeaGraphGraph Computing

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GeaGraph: Large-Scale Graph Computing System Wins World Internet Conference Award

DataFunTalk

Jun 2, 2021 · Artificial Intelligence

Industrial-Scale Graph Learning for JD Advertising: 9N GRAPH End‑to‑End Solution and BVSHG Model

This article introduces JD.com's 9N GRAPH industrialization framework for large‑scale graph algorithms in advertising, covering the challenges of e‑commerce recommendation, the end‑to‑end solution architecture, the BVSHG multi‑behavior heterogeneous GNN model, training pipelines, and observed business impact.

BVSHGGraph Neural NetworksJD.com

0 likes · 17 min read

Industrial-Scale Graph Learning for JD Advertising: 9N GRAPH End‑to‑End Solution and BVSHG Model

AntTech

Oct 30, 2019 · Artificial Intelligence

Financial Graph Machine Learning, AutoML, and Multi‑Agent Reinforcement Learning at Ant Financial

Professor Song Le presented at the Cloudwise Conference how Ant Financial leverages large‑scale graph neural networks, automated machine‑learning platforms, and multi‑agent reinforcement learning to model complex financial networks, improve risk control, and drive diverse fintech applications.

AutoMLFinancial AIGraph Neural Networks

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Financial Graph Machine Learning, AutoML, and Multi‑Agent Reinforcement Learning at Ant Financial