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Mar 28, 2024 · Artificial Intelligence

Large-Scale Heterogeneous Graph Modeling and GraphET Engine for Meituan Food Delivery Search Advertising

The paper describes how Meituan’s food‑delivery search advertising uses a heterogeneous billion‑node graph and the GraphET engine to boost weak‑supply recall, detailing a progression from fine‑grained modeling to GPT‑enhanced pre‑training, and presenting a scalable training and low‑latency inference architecture that handles hundreds of billions of edges.

GraphETLarge-Scale GraphMeituan

0 likes · 27 min read

Large-Scale Heterogeneous Graph Modeling and GraphET Engine for Meituan Food Delivery Search Advertising

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 LibraryDistributed 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 ArchitectureLarge-Scale Graph

0 likes · 15 min read

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.

Large-Scale GraphSampling Optimizationdistributed computing

0 likes · 13 min read

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

ITPUB

May 27, 2022 · Databases

How HugeGraph’s Self‑Built Graph Computing Tackles Large‑Scale Graph Challenges

This article explains the fundamentals of graph computing, compares it with traditional processing, outlines industry challenges such as partitioning and load imbalance, and details HugeGraph’s self‑developed architecture, key technical solutions, and how developers can create and deploy graph algorithms.

Algorithm DevelopmentData PartitioningGraph Database

0 likes · 14 min read

How HugeGraph’s Self‑Built Graph Computing Tackles Large‑Scale Graph Challenges

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 DataGeaGraphIndustry-Academia Collaboration

0 likes · 8 min read

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.

BVSHGIndustrial AIJD.com

0 likes · 17 min read

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

ITFLY8 Architecture Home

Mar 16, 2021 · Artificial Intelligence

How NetEase Cloud Music Solved Cold‑Start with Large‑Scale Graph Neural Networks

This article explains how NetEase Cloud Music tackled cold‑start recommendation challenges in live streaming by leveraging Baidu's PGL distributed graph learning framework to train massive graph neural networks that transfer user behavior from music domains to live content, achieving significant performance gains.

AIDistributed TrainingLarge-Scale Graph

0 likes · 7 min read

How NetEase Cloud Music Solved Cold‑Start with Large‑Scale Graph Neural Networks

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.

Ant FinancialLarge-Scale Graphgraph neural networks

0 likes · 12 min read

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

WeChat Backend Team

Jan 18, 2018 · Information Security

How WeChat Detects Anomalous Users at Billion‑Scale: Inside Its Fast, Scalable Framework

This article explains how WeChat’s security team builds a scalable anomaly‑detection framework that partitions billions of user accounts, weights suspicious attributes, computes similarity graphs, and leverages Spark optimizations and graph‑partitioning techniques to efficiently identify malicious user clusters.

Large-Scale GraphSecuritySpark optimization

0 likes · 18 min read

How WeChat Detects Anomalous Users at Billion‑Scale: Inside Its Fast, Scalable Framework