Tagged articles

Graph Neural Networks

178 articles · Page 2 of 2

May 18, 2022 · Artificial Intelligence

Overview of Document Intelligence Models: StrucText, LayoutLMv3, and GraphDoc

This article reviews three representative document intelligence models—StrucText, LayoutLMv3, and GraphDoc—detailing their input features, feature fusion strategies, self‑supervised tasks, and underlying architectures, and explains how they learn embeddings for segments, words, or regions to enable classification and key‑value extraction.

Graph Neural NetworksLayout AnalysisMultimodal

0 likes · 15 min read

Overview of Document Intelligence Models: StrucText, LayoutLMv3, and GraphDoc

DataFunSummit

May 10, 2022 · Artificial Intelligence

Optimizing Fliggy Search Ranking with Product Inclusion Relationships: The DIRN Model

This article presents the DIRN model, which leverages product inclusion graphs and graph‑based embeddings to address the challenges of ranking both single‑item and complex travel products on Fliggy, demonstrating significant CTR, CVR, and GMV improvements through offline experiments and online A/B testing.

AlibabaDIRNGraph Neural Networks

0 likes · 13 min read

Optimizing Fliggy Search Ranking with Product Inclusion Relationships: The DIRN Model

DataFunTalk

May 1, 2022 · Artificial Intelligence

Graph Deep Learning for Natural Language Processing: Methods, Models, and the Graph4NLP Library

This talk introduces graph deep learning techniques for natural language processing, covering the motivation for graph representations, traditional graph-based NLP methods, fundamentals of graph neural networks, static and dynamic graph construction, representation learning, and showcases the open‑source Graph4NLP Python library with example applications.

Graph Neural NetworksGraph RepresentationGraph4NLP

0 likes · 16 min read

Graph Deep Learning for Natural Language Processing: Methods, Models, and the Graph4NLP Library

DataFunSummit

Mar 26, 2022 · Artificial Intelligence

Deep Learning‑Based Design of Financial Index Funds Using Graph Neural Networks

This talk presents a deep‑learning framework that formulates financial index‑fund construction as a sparse portfolio optimization problem, solves the mixed‑integer programming via a two‑stage graph‑neural‑network pipeline, and demonstrates superior tracking performance and scalability on large‑scale index datasets.

AI financeDeep LearningGraph Neural Networks

0 likes · 16 min read

Deep Learning‑Based Design of Financial Index Funds Using Graph Neural Networks

Kuaishou Tech

Mar 23, 2022 · Artificial Intelligence

Graph-Based I2I Recall for Short Video Recommendation at Kuaishou

This article explains how Kuaishou leverages graph neural networks for item‑to‑item (I2I) recall in short‑video recommendation, detailing the system background, pipeline architecture, optimization techniques such as similarity measurement, graph structure learning, edge‑weight learning, and future research directions.

.aiEmbeddingGraph Neural Networks

0 likes · 17 min read

Graph-Based I2I Recall for Short Video Recommendation at Kuaishou

DataFunSummit

Mar 6, 2022 · Artificial Intelligence

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

This article traces the development of embedding methods—from the early word2vec model through item2vec, DeepWalk, Node2vec, EGES, HERec, GraphRT, and target‑fitting approaches like DSSM and YouTube recommendation—highlighting how sequence‑construction and target‑fitting paradigms have shaped modern recommendation systems and AI applications.

Deep LearningEmbeddingGraph Neural Networks

0 likes · 26 min read

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

DataFunSummit

Feb 26, 2022 · Artificial Intelligence

Graph-Based Sparse Behavior Recall Models for Content Recommendation

This article presents a comprehensive study of graph‑based recall techniques for content recommendation, detailing how knowledge‑graph‑augmented user‑behavior graphs and novel attention‑driven models such as GADM, SGGA, and SGGGA improve performance for users with sparse interaction histories.

Attention MechanismDeep LearningGraph Neural Networks

0 likes · 11 min read

Graph-Based Sparse Behavior Recall Models for Content Recommendation

DataFunSummit

Feb 25, 2022 · Artificial Intelligence

Knowledge Graph Representation and Reasoning Forum at DataFun Summit 2022

The DataFun Summit 2022 Knowledge Graph Forum, held on March 12, presents cutting‑edge research on knowledge graph representation learning, multi‑hop reasoning, temporal KG question answering, and their applications in finance and retail, featuring talks by leading experts from JD, Fourth Paradigm, Stanford, and Meituan.

AI ApplicationsGraph Neural Networksknowledge graph

0 likes · 9 min read

DataFunSummit

Feb 22, 2022 · Artificial Intelligence

Graph Pretraining Techniques for Molecular Representation and Their Applications in Drug Discovery

This article reviews the motivation, methods, and results of graph-based self‑supervised pretraining for molecular data, introduces the ChemRL‑GEM model that incorporates 3‑D structural information, and demonstrates its superior performance on ADMET, affinity prediction, and benchmark competitions using the PaddleHelix platform.

.aiChemistryGraph Neural Networks

0 likes · 18 min read

Graph Pretraining Techniques for Molecular Representation and Their Applications in Drug Discovery

DaTaobao Tech

Feb 22, 2022 · Artificial Intelligence

Graph-based Deep Recall Models for Sparse User Behavior in Content Recommendation

The paper proposes graph‑based deep recall models that enrich sparse user behavior sequences in video recommendation by integrating content knowledge graphs and adaptive attention mechanisms, demonstrating that variants such as GADM, SGGA, and SGGGA significantly boost click‑through rates in online experiments.

Graph Neural NetworksRecommendation Systemsattention

0 likes · 11 min read

Graph-based Deep Recall Models for Sparse User Behavior in Content Recommendation

DataFunTalk

Feb 16, 2022 · Artificial Intelligence

Deep Learning-Based Design of Financial Index Funds Using Graph Neural Networks

Recent advances in deep learning have enabled a novel two‑stage approach for designing financial index funds, where graph neural networks first select a sparse set of assets and then allocate weights, dramatically reducing computational complexity while achieving performance comparable to traditional mixed‑integer programming methods.

Graph Neural NetworksIndex FundsOptimization

0 likes · 17 min read

DataFunSummit

Feb 15, 2022 · Artificial Intelligence

Real-time Fraud Detection in E-commerce Payments Using Graph Neural Networks

This article presents an end‑to‑end solution that leverages graph neural networks and dynamic bipartite graph construction to detect payment fraud in eBay's e‑commerce platform in real time, addressing traditional model limitations, graph latency challenges, and demonstrating superior performance over GBDT approaches.

Graph Neural Networkse-commercefraud detection

0 likes · 15 min read

Real-time Fraud Detection in E-commerce Payments Using Graph Neural Networks

DataFunSummit

Feb 10, 2022 · Artificial Intelligence

Baidu's PGL2.2: A Graph Neural Network Framework, Techniques, and Real‑World Applications

This article introduces Baidu's PGL2.2 graph learning platform, explains graph modeling and message‑passing GNN techniques, details training strategies for small, medium and large graphs, showcases node classification and link‑prediction methods, and describes how the framework is applied in search, recommendation, risk control, and knowledge‑graph competitions.

Graph Neural NetworksLarge‑Scale TrainingPGL2.2

0 likes · 15 min read

Baidu's PGL2.2: A Graph Neural Network Framework, Techniques, and Real‑World Applications

Alimama Tech

Feb 9, 2022 · Artificial Intelligence

Alibaba Mama Team Papers Selected for The Web Conference 2023 – Summaries of Five AI Research Works

The Alibaba Mama technical team secured five paper acceptances at The Web Conference 2023, presenting advances in unbiased delayed‑feedback conversion modeling, uncertainty‑regularized knowledge‑distilled CVR debiasing, feature‑aware probability calibration, coordinated two‑stage ad auctions, and scalable decoupled graph neural networks for large‑scale e‑commerce retrieval.

.aiAuction DesignCVR

0 likes · 12 min read

Alibaba Mama Team Papers Selected for The Web Conference 2023 – Summaries of Five AI Research Works

JD Retail Technology

Jan 24, 2022 · Artificial Intelligence

Galileo: An Open‑Source Scalable Graph Deep Learning Framework for Industrial‑Scale Applications

Galileo is an open‑source, distributed graph deep‑learning framework that supports ultra‑large heterogeneous graphs, dual TensorFlow/PyTorch back‑ends, and a flexible API, enabling fast prototyping of graph neural networks such as HeteSAGE for real‑world recommendation and other AI scenarios.

AI FrameworkGalileoGraph Neural Networks

0 likes · 11 min read

Galileo: An Open‑Source Scalable Graph Deep Learning Framework for Industrial‑Scale Applications

Alimama Tech

Jan 19, 2022 · Artificial Intelligence

Advances in Alibaba Search Advertising Estimation: Model Deepening, Interaction, and System Efficiency (2021 Review)

The 2021 review of Alibaba’s Mama Search Advertising estimation platform details advances in model deepening—such as hash‑based embedding compression, adaptive dynamic parameters and graph neural networks—model interaction via a multi‑stage cascade with ranking distillation and oracle bias, and system efficiency gains from HPC training, mixed‑precision, multi‑hash embeddings, and fp16 quantization that deliver roughly a thirty‑fold speed‑up.

Ad TechCTRCVR

0 likes · 34 min read

Advances in Alibaba Search Advertising Estimation: Model Deepening, Interaction, and System Efficiency (2021 Review)

DataFunTalk

Jan 17, 2022 · Artificial Intelligence

Graph Attention Multi‑Layer Perceptron (GAMLP) and Node‑Dependent Local Smoothing (NDLS) for Scalable and Flexible Graph Neural Networks

This talk introduces the motivation, design, theoretical analysis, and extensive experimental results of Tencent Angel Graph's Graph Attention Multi‑Layer Perceptron (GAMLP) and Node‑Dependent Local Smoothing (NDLS), which address GNN scalability and flexibility by using node‑wise adaptive propagation, attention‑based feature fusion, and a lightweight training pipeline.

Attention MechanismGAMLPGraph Neural Networks

0 likes · 18 min read

Graph Attention Multi‑Layer Perceptron (GAMLP) and Node‑Dependent Local Smoothing (NDLS) for Scalable and Flexible Graph Neural Networks

DataFunTalk

Jan 13, 2022 · Artificial Intelligence

Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources

This article provides a comprehensive overview of fraud detection using graph neural networks, covering background definitions, fraud categories, GNN application steps, a timeline of key research papers, practical challenges, solutions, and a collection of open‑source resources and datasets.

.aiGraph Neural Networksfraud detection

0 likes · 24 min read

Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources

ByteDance Terminal Technology

Jan 11, 2022 · Information Security

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Work

This article presents a comprehensive study on detecting malicious webpages using heterogeneous graph structures and Graph Convolutional Networks, detailing background challenges, technical approaches, model iterations, optimization techniques for large‑scale deployment, experimental results, and directions for future research.

GCNGraph Neural NetworksOptimization

0 likes · 9 min read

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Work

DataFunSummit

Jan 9, 2022 · Artificial Intelligence

Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources

This article reviews the fundamentals of fraud, surveys the evolution of graph neural network research for fraud detection, outlines practical application steps, discusses key challenges such as disguise, scalability, and label scarcity, and provides representative papers, new research directions, industrial case studies, and open-source resources.

.aiGNNGraph Neural Networks

0 likes · 23 min read

Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources

DataFunSummit

Jan 8, 2022 · Artificial Intelligence

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

This article introduces graph representation learning, explains the Graph Information Bottleneck (GIB) framework for obtaining robust graph embeddings, and presents AD‑GCL, a contrastive learning method that leverages GIB principles to improve graph neural network performance without requiring task labels.

Graph Neural NetworksGraph RepresentationRobustness

0 likes · 15 min read

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

ByteDance Terminal Technology

Jan 7, 2022 · Information Security

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions

This article presents a comprehensive study on detecting malicious webpages by constructing heterogeneous graphs from URL redirection and textual features, applying Graph Convolutional Networks and Cluster‑Text‑GCN models, detailing optimization techniques for large‑scale deployment, and outlining future research directions.

GCNGraph Neural NetworksText Classification

0 likes · 11 min read

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions

DataFunTalk

Jan 5, 2022 · Artificial Intelligence

Graph-Based Methods for Hot Event Discovery, Long Text Matching, and Ontology Construction in Natural Language Processing

This talk presents a series of graph‑based techniques for natural language processing, including the Story Forest system for hot event discovery, the GIANT framework for ontology creation and user interest modeling, and a divide‑and‑conquer approach to long‑text matching that leverages graph neural networks and community detection.

Graph Neural Networksevent detectiongraph mining

0 likes · 19 min read

Graph-Based Methods for Hot Event Discovery, Long Text Matching, and Ontology Construction in Natural Language Processing

Code DAO

Dec 25, 2021 · Artificial Intelligence

Understanding Graph Neural Networks: Nodes, Edges, and Message Passing

This article explains the fundamentals of graph neural networks, covering graph concepts, node classification via neighborhood aggregation, message‑passing mechanics, mathematical notation, a full DGL‑PyTorch implementation on the Reddit dataset, and training results showing accuracy improvements up to 91 %.

DGLGCNGNN

0 likes · 9 min read

Understanding Graph Neural Networks: Nodes, Edges, and Message Passing

Beike Product & Technology

Dec 23, 2021 · Artificial Intelligence

Highlights from the CNCC 2021 Knowledge Graph and Graph Machine Learning Forum

The CNCC 2021 forum brought together leading academics and industry experts to discuss advances in graph neural networks, graph computing for quantum chemistry, and practical applications of knowledge‑graph reasoning in sectors such as real‑estate and online video, showcasing both research breakthroughs and industrial deployment strategies.

Artificial IntelligenceGraph Neural NetworksQuantum Chemistry

0 likes · 8 min read

Highlights from the CNCC 2021 Knowledge Graph and Graph Machine Learning Forum

DataFunTalk

Dec 11, 2021 · Artificial Intelligence

Graph Information Bottleneck (GIB) and AD‑GCL: Enhancing Graph Representation Learning and Robustness

This article introduces graph representation learning, explains the Graph Information Bottleneck (GIB) framework for obtaining robust graph embeddings, and presents AD‑GCL, a contrastive learning method that leverages GIB principles to improve robustness and performance without requiring task labels.

Graph Neural NetworksRobustnesscontrastive learning

0 likes · 16 min read

Graph Information Bottleneck (GIB) and AD‑GCL: Enhancing Graph Representation Learning and Robustness

DataFunTalk

Dec 4, 2021 · Artificial Intelligence

Knowledge‑Enhanced Graph Semantic Understanding and the ERNIESage Framework

This article introduces knowledge‑enhanced graph semantic understanding techniques, detailing the ERNIE pre‑training models, GraphSAGE, and the ERNIESage family of node, edge, and multi‑neighbor architectures, and demonstrates their explicit and implicit knowledge integration in industrial search, recommendation, and map‑POI applications.

ERNIEGraph Neural NetworksIndustrial Applications

0 likes · 14 min read

Knowledge‑Enhanced Graph Semantic Understanding and the ERNIESage Framework

DataFunTalk

Dec 1, 2021 · Artificial Intelligence

Awesome Knowledge Graph Resources: Papers, Tools, Datasets, and Projects

This article presents a curated collection of high‑star GitHub "awesome" repositories covering knowledge graph fundamentals, relation extraction, KG‑QA, graph construction, graph neural networks, dynamic graph learning, and multimodal knowledge graphs, providing links, summaries, and key resources for researchers and practitioners.

AI resourcesAwesome ListGraph Neural Networks

0 likes · 12 min read

Awesome Knowledge Graph Resources: Papers, Tools, Datasets, and Projects

DataFunTalk

Nov 26, 2021 · Artificial Intelligence

Graph Neural Networks for Molecular Networks and Drug Discovery

This presentation by Stanford PhD student Huang Kexin explores the challenges and innovations of applying graph machine learning to molecular and biomedical networks, introducing specialized GNN architectures, actionable hypothesis generation, domain‑scientist interfaces, few‑shot learning, and the Therapeutics Data Commons for accelerating drug discovery.

Graph Neural Networksbioinformaticsbiomedical AI

0 likes · 9 min read

Graph Neural Networks for Molecular Networks and Drug Discovery

DataFunSummit

Nov 26, 2021 · Artificial Intelligence

Graph Machine Learning for Molecular Networks: Challenges, Methods, and Applications in Biomedicine

This talk by a Stanford PhD student explores how graph neural networks can be adapted for molecular and biomedical networks, discusses the limitations of standard GNNs, introduces novel methods such as SkipGNN and G‑Meta, and demonstrates their use for drug‑drug interaction prediction, hypothesis generation, and treatment discovery with few‑shot learning.

Biomedical ApplicationsDrug InteractionGraph Neural Networks

0 likes · 9 min read

Graph Machine Learning for Molecular Networks: Challenges, Methods, and Applications in Biomedicine

DataFunTalk

Nov 11, 2021 · Artificial Intelligence

Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology

This article details how Shuhe Technology leveraged large‑scale graph neural networks, built with DGL and PyTorch, to improve financial fraud detection by preparing massive relationship graphs, pruning sparse nodes, extracting rich features, addressing class imbalance, and achieving a stable AUC gain of about four points.

DGLGATGNN

0 likes · 12 min read

Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology

DataFunSummit

Nov 10, 2021 · Artificial Intelligence

Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology

This article describes how Shuhe Technology leveraged graph neural networks to improve financial risk assessment by preparing massive relational graph data, selecting DGL as the development framework, designing a GraphSage‑GAT model, addressing data sparsity and imbalance, and achieving notable AUC gains over traditional methods.

.aiGNNGraph Neural Networks

0 likes · 12 min read

Aotu Lab

Oct 28, 2021 · Fundamentals

Exploring HarmonyOS ACE UI, React useEffect Pitfalls, Three.js 3D, and AI Basics

This issue presents deep dives into HarmonyOS ACE UI architecture, common infinite-loop pitfalls of React’s useEffect hook, a Three.js tutorial for building a 3D room, an introductory guide to graph neural networks, and a fundamentals‑focused machine‑learning primer, offering practical insights across front‑end, graphics, and AI domains.

.aiGraph Neural NetworksHarmonyOS

0 likes · 6 min read

Exploring HarmonyOS ACE UI, React useEffect Pitfalls, Three.js 3D, and AI Basics

DataFunTalk

Oct 26, 2021 · Artificial Intelligence

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems

This article explains how contrastive learning, originally popular in computer‑vision, can be interpreted and applied to recommendation‑system recall and coarse‑ranking models, covering its theoretical roots, typical architectures like SimCLR, MoCo and SwAV, and practical tricks such as in‑batch negatives, embedding normalization, temperature scaling, and graph‑based extensions.

Graph Neural NetworksRecommendation Systemscontrastive learning

0 likes · 40 min read

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems

DataFunTalk

Oct 19, 2021 · Artificial Intelligence

Graph Contrastive Learning: Foundations, Methods, and Recent Advances (GRACE & GCA)

This article reviews recent research on graph self‑supervised learning, focusing on contrastive learning fundamentals, the SimCLR‑style framework, representative models such as GRACE and its adaptive augmentation extension GCA, experimental evaluations, and future directions for graph contrastive methods.

GCAGraceGraph Neural Networks

0 likes · 16 min read

Graph Contrastive Learning: Foundations, Methods, and Recent Advances (GRACE & GCA)

Baidu Intelligent Testing

Oct 19, 2021 · Artificial Intelligence

Graph-Based Anti-Fraud: Gang Mining and Node Representation for Account Security

This article describes how Baidu's account security team leverages large‑scale graph technology and graph neural networks to detect and characterize black‑industry cheating gangs, presents a customized GraphSAGE link‑prediction model, and evaluates its superiority over MLP and GCN embeddings for downstream risk‑control tasks.

Graph Neural NetworksNode Representationanti-fraud

0 likes · 12 min read

Graph-Based Anti-Fraud: Gang Mining and Node Representation for Account Security

Baidu Geek Talk

Sep 29, 2021 · Artificial Intelligence

Graph-Based Anti-Fraud: Gang Mining and Node Representation Using Graph Neural Networks

To curb large‑scale, organized fraud on Baidu’s platform, the Account Security team built a scalable heterogeneous graph framework that links accounts, features, and devices, trains GraphSAGE‑based node embeddings via link‑prediction, and leverages these representations to uncover fraud gangs, boosting detection accuracy above 90% across billions of nodes.

Graph Neural Networksanti-fraudgraph mining

0 likes · 13 min read

Graph-Based Anti-Fraud: Gang Mining and Node Representation Using Graph Neural Networks

DataFunTalk

Sep 28, 2021 · Artificial Intelligence

Graph Modeling and GCN Exploration at 极验: Evolution, Offline and Real‑time Solutions

The talk presents an overview of graph neural network development, explains 极验's graph modeling research and evolution, and details offline and real‑time GCN solutions, including self‑supervised training, large‑scale handling, and performance comparisons, highlighting practical applications in fraud detection and risk control.

Anomaly DetectionGCNGraph Modeling

0 likes · 26 min read

Graph Modeling and GCN Exploration at 极验: Evolution, Offline and Real‑time Solutions

Alimama Tech

Sep 15, 2021 · Artificial Intelligence

Alibaba Mama Open‑sources Curvature Learning Framework and Federated Learning Solution

Alibaba Mama announced the open‑source release of its Curvature Space Learning Framework and a Federated Learning Solution, promising up to 80% storage savings, 15% matching‑precision gains, enhanced privacy through federated computation, and broader applicability across search advertising, logistics, finance and healthcare.

.aiCurvature LearningGraph Neural Networks

0 likes · 8 min read

Alibaba Mama Open‑sources Curvature Learning Framework and Federated Learning Solution

DataFunTalk

Sep 3, 2021 · Artificial Intelligence

Construction and Application of an Interest Point Graph for Content Understanding in Information Feed Recommendation

This article explains how large‑scale UGC data is used to build a multi‑type interest point graph, describes the mining, hierarchical and associative relationship extraction methods, and demonstrates how the graph improves content understanding and recommendation accuracy while mitigating filter‑bubble effects.

Artificial IntelligenceGraph Neural NetworksInformation Retrieval

0 likes · 25 min read

Construction and Application of an Interest Point Graph for Content Understanding in Information Feed Recommendation

DataFunTalk

Aug 23, 2021 · Artificial Intelligence

Graph Data Analysis and Graph Neural Network Applications Across Multiple Scenarios

This article introduces graph fundamentals, various application scenarios such as science, code logic, Spark workflows, social networks, and event graphs, then details graph data modeling, analysis, matrix computations, and the deployment of graph neural networks using frameworks like DGL, highlighting practical engineering considerations.

.aiDGLGraph Neural Networks

0 likes · 16 min read

Graph Data Analysis and Graph Neural Network Applications Across Multiple Scenarios

Alimama Tech

Aug 18, 2021 · Artificial Intelligence

Overview of Recent Alibaba Mama Research Papers on AI and Large‑Scale Advertising Systems

The article surveys six Alibaba Mama papers accepted at CIKM 2021, presenting novel AI methods—including a heterogeneous graph neural network for keyword matching, a star‑topology multi‑domain CTR model, a compact hash embedding technique, adaptive masked twins layers, automated hierarchical conversion prediction, and a scalable multi‑view ad retrieval system—each demonstrating substantial online performance improvements and large‑scale deployment.

.aiAdvertisingCTR Prediction

0 likes · 11 min read

Overview of Recent Alibaba Mama Research Papers on AI and Large‑Scale Advertising Systems

DataFunTalk

Jul 22, 2021 · Artificial Intelligence

Joint Entity and Relation Extraction: Methods, Challenges, and Document‑Level Approaches

This article reviews the fundamentals of entity‑relation extraction, surveys joint extraction techniques such as sequence labeling, table‑filling and seq2seq models, discusses document‑level graph‑based methods, highlights experimental findings, and outlines future research directions in knowledge‑graph construction.

Graph Neural NetworksNLPdocument-level

0 likes · 17 min read

Joint Entity and Relation Extraction: Methods, Challenges, and Document‑Level Approaches

DataFunTalk

Jul 15, 2021 · Artificial Intelligence

Graph Neural Network‑Based Payment Fraud Detection at eBay

The article explains how eBay protects its global payment system using a graph‑neural‑network driven risk management framework called xFraud, which combines heterogeneous graph sampling, node‑type encoding, attention mechanisms and dynamic‑graph extensions to detect and explain both individual and organized fraud patterns in real‑time.

Graph Neural NetworkseBaymachine learning

0 likes · 13 min read

Graph Neural Network‑Based Payment Fraud Detection at eBay

DataFunTalk

Jul 7, 2021 · Artificial Intelligence

Robust Graph Representation Learning via Neural Sparsification

NeuralSparse is a supervised graph sparsification framework that removes task-irrelevant edges to improve GNN generalization, combining a sparsification network with downstream GNN training, and demonstrates superior performance across multiple graph benchmarks compared to random edge dropping and other sparsification methods.

Edge PruningGraph Neural NetworksGraph Representation

0 likes · 8 min read

Robust Graph Representation Learning via Neural Sparsification

Kuaishou Tech

Jul 7, 2021 · Artificial Intelligence

SURGE: A Graph Neural Network Based Sequential Recommendation Framework

The SURGE framework leverages graph neural networks to construct and pool interest graphs from user interaction sequences, achieving stable and fast convergence, robust long‑sequence modeling, and significant performance gains over existing sequential recommendation methods on e‑commerce and short‑video datasets.

Graph Neural NetworksLong SequencesSURGE

0 likes · 12 min read

SURGE: A Graph Neural Network Based Sequential Recommendation Framework

360 Tech Engineering

Jul 2, 2021 · Artificial Intelligence

DGL Operator: A Kubernetes‑Native Solution for Distributed Graph Neural Network Training

The article introduces DGL Operator, an open‑source Kubernetes‑based controller that automates the lifecycle of distributed graph neural network training with DGL, explains its terminology, challenges of native DGL distribution, and provides detailed architecture, workflow, and YAML/CLI examples for easy deployment.

.aiDGLGraph Neural Networks

0 likes · 18 min read

DGL Operator: A Kubernetes‑Native Solution for Distributed Graph Neural Network Training

360 Smart Cloud

Jul 1, 2021 · Cloud Native

DGL Operator: A Kubernetes Native Controller for Distributed Graph Neural Network Training

DGL Operator is an open‑source Kubernetes controller that automates the lifecycle of distributed graph neural network training by handling configuration generation, graph partitioning, training execution, and resource cleanup, providing a cloud‑native solution for large‑scale GNN workloads.

.aiCloud NativeDGL

0 likes · 20 min read

DGL Operator: A Kubernetes Native Controller for Distributed Graph Neural Network Training

DataFunTalk

Jun 22, 2021 · Artificial Intelligence

Survey of Graph Neural Networks for Natural Language Processing

This comprehensive survey reviews the latest research on graph neural networks applied to natural language processing, covering graph construction methods, graph representation learning techniques, encoder‑decoder models, static and dynamic graph building, and discusses challenges, benchmarks, and future directions in the field.

Encoder-DecoderGraph ConstructionGraph Neural Networks

0 likes · 57 min read

Survey of Graph Neural Networks for Natural Language Processing

DataFunTalk

Jun 1, 2021 · Artificial Intelligence

Advances in Click‑Through Rate (CTR) Modeling: Optimizations Across Embedding, Hidden, and Output Layers

This article reviews recent academic and industrial advances in click‑through rate prediction, classifying optimization techniques for the three‑layer CTR architecture—Embedding, Hidden, and Output—while summarizing three SIGIR papers on graph‑based user behavior modeling, explicit semantic cross‑feature learning, and learnable feature selection for pre‑ranking.

CTRGraph Neural Networksclick-through rate

0 likes · 11 min read

Advances in Click‑Through Rate (CTR) Modeling: Optimizations Across Embedding, Hidden, and Output Layers

58 Tech

Apr 16, 2021 · Artificial Intelligence

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services

The article presents a comprehensive anti‑fraud framework that analyzes black‑market fraud characteristics, reviews conventional fraud‑mitigation methods, and proposes a multimodal graph‑neural‑network approach—leveraging device, behavior, and content similarity—to accurately identify fraudulent users on large‑scale internet platforms.

Graph Neural NetworksMultimodal Dataanti‑fraud

0 likes · 18 min read

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services

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.

.aiGraph Neural NetworksLarge-Scale Graph

0 likes · 7 min read

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

AntTech

Feb 24, 2021 · Artificial Intelligence

Ant Group's Self‑Developed Graph Neural Network Research: GeniePath and Bandit Sampler

This article introduces the fundamentals of graph neural networks, explains their expressive power for relational risk identification, and details Ant Group's innovations—including the GeniePath architecture and a bandit‑based sampling optimizer—that achieve superior performance on benchmark datasets.

GNNGeniePathGraph Neural Networks

0 likes · 7 min read

Ant Group's Self‑Developed Graph Neural Network Research: GeniePath and Bandit Sampler

DataFunTalk

Dec 31, 2020 · Artificial Intelligence

Introduction to Graph Neural Networks and Their Applications in Recommendation Systems

This article introduces graph neural networks, explains their underlying sampling and aggregation mechanisms, and demonstrates how they are applied in large‑scale recommendation scenarios such as video and content feeds at Tencent, highlighting practical results and lessons learned.

Artificial IntelligenceBig DataGraph Neural Networks

0 likes · 10 min read

Introduction to Graph Neural Networks and Their Applications in Recommendation Systems

Alibaba Cloud Developer

Dec 17, 2020 · Big Data

Why GraphScope is Revolutionizing Large-Scale Graph Computing for AI and Big Data

GraphScope, an open‑source one‑stop platform from Alibaba DAMO Academy, unifies interactive queries, graph analytics, and graph learning on massive, rapidly evolving graphs, offering high‑performance distributed memory management, Gremlin optimization, and seamless Python integration to tackle real‑world AI and big‑data challenges.

Big DataGraph Neural NetworksOpen-source

0 likes · 21 min read

Why GraphScope is Revolutionizing Large-Scale Graph Computing for AI and Big Data

DataFunTalk

Dec 1, 2020 · Artificial Intelligence

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

This article systematically reviews mainstream embedding technologies—including matrix factorization, static and dynamic word embeddings, and graph‑based methods—explaining their principles, implementations, and practical applications in recommendation, advertising, and search systems.

EmbeddingGraph Neural NetworksRecommendation Systems

0 likes · 32 min read

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

58 Tech

Nov 18, 2020 · Artificial Intelligence

Applying Graph Algorithms and Graph Convolutional Networks to Advertising Anti‑Fraud

This article describes how graph theory and graph convolutional neural networks are leveraged to model user‑IP relationships, detect fraudulent advertising clusters, and improve detection accuracy and recall through a combination of unsupervised graph algorithms and supervised GCN training in a large‑scale ad‑anti‑fraud system.

AdvertisingGCNGraph Neural Networks

0 likes · 14 min read

Applying Graph Algorithms and Graph Convolutional Networks to Advertising Anti‑Fraud

JD Cloud Developers

Nov 16, 2020 · Artificial Intelligence

AI‑Powered Supply Chain, LinkedIn’s Dagli ML Library, and Latest GNN Research

This week’s developer roundup covers JD’s open AI‑driven supply‑chain decision engine, LinkedIn’s Dagli Java machine‑learning library, PayPal’s crypto trading rollout, SpaceX’s Starlink service approval in Canada, AppleWatch’s NightWare nightmare‑interrupt feature, Intel’s new server GPU, and two cutting‑edge GNN research papers on multivariate time‑series forecasting and molecular generation.

.aiCryptocurrencyGraph Neural Networks

0 likes · 9 min read

AI‑Powered Supply Chain, LinkedIn’s Dagli ML Library, and Latest GNN Research

JD Retail Technology

Oct 21, 2020 · Artificial Intelligence

Galileo: A Distributed Graph Deep Learning Framework for Large‑Scale Industrial Scenarios

The article introduces Galileo, JD Retail's distributed graph deep‑learning platform that supports heterogeneous and dynamic graphs, ultra‑large scale training, flexible model customization, and seamless integration with TensorFlow and PyTorch, highlighting its architecture, core challenges, built‑in algorithms, and upcoming open‑source release.

AI platformGraph Neural Networksdistributed training

0 likes · 11 min read

Galileo: A Distributed Graph Deep Learning Framework for Large‑Scale Industrial Scenarios

DataFunTalk

Oct 18, 2020 · Artificial Intelligence

Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques

This article reviews how skip‑gram‑based graph embedding methods such as DeepWalk, LINE and node2vec can be interpreted as matrix factorization, explains the NetMF and NetSMF frameworks that use sparse matrix approximations and random SVD for large‑scale networks, and discusses extensions like GATNE and deep clustering approaches to address practical challenges in constructing and applying graph representations.

Graph Neural NetworksSkip-gramgraph embedding

0 likes · 13 min read

Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques

Meituan Technology Team

Aug 27, 2020 · Artificial Intelligence

Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications

The team built an automated graph learning framework that preprocesses diverse graphs, employs four GNN architectures, conducts rapid hyper‑parameter tuning, and fuses models with density‑aware weighting, securing first place in KDD Cup 2020 AutoGraph and boosting Meituan’s ad recall and CTR prediction.

AutoMLGraph Neural NetworksKDD Cup

0 likes · 30 min read

Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications

Youku Technology

Aug 6, 2020 · Artificial Intelligence

Recent ACM MM Papers Accepted by Alibaba Entertainment Group

Alibaba Entertainment Group secured four ACM MM paper acceptances, presenting a probabilistic graphical model for crowdsourced visual quality assessment, an attention‑driven Siamese network with reinforcement learning for robust object tracking, a scene‑aware context‑graph method for unsupervised video anomaly detection, and a cross‑modal graph‑matching approach for visual grounding.

Graph Neural NetworksObject TrackingVisual Grounding

0 likes · 6 min read

Recent ACM MM Papers Accepted by Alibaba Entertainment Group

Tencent Advertising Technology

Jun 22, 2020 · Artificial Intelligence

Graph-based Evidence Aggregating and Reasoning (GEAR) Model for Fact Verification in NLP

The article explains how the GEAR model uses graph neural networks and BERT representations to aggregate multiple pieces of evidence for fact verification, improving accuracy on datasets like FEVER and offering applications in misinformation detection, knowledge‑graph completion, and advertising analytics.

BERTGEAR modelGraph Neural Networks

0 likes · 8 min read

Graph-based Evidence Aggregating and Reasoning (GEAR) Model for Fact Verification in NLP

DataFunTalk

May 23, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

Based on academic and industry experience, iQIYI has designed a deep semantic representation learning framework that integrates multimodal side information and deep models such as Transformers and graph neural networks, improving recall, ranking, deduplication, diversity and semantic matching across recommendation and search scenarios.

Deep LearningGraph Neural NetworksMultimodal

0 likes · 27 min read

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

iQIYI Technical Product Team

May 15, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework: Design, Challenges, and Applications

iQIYI’s deep semantic representation learning framework integrates multimodal content, knowledge graphs, and user behavior through layered data, feature, strategy, and application components, employing early, late, and hybrid fusion with Transformers, GCNs, and other deep models to deliver high‑quality embeddings that boost recommendation, search, and streaming performance across dozens of business scenarios.

Graph Neural NetworksMultimodalSearch

0 likes · 28 min read

iQIYI Deep Semantic Representation Learning Framework: Design, Challenges, and Applications

Alibaba Cloud Developer

Apr 9, 2020 · Artificial Intelligence

How Alibaba’s Open‑Source Graph‑Learn Accelerates GNN Deployment

Alibaba’s open‑source Graph‑Learn framework brings industrial‑scale graph neural network capabilities to production by offering lightweight portability, modular extensibility, reusable interfaces, and seamless integration with major deep‑learning ecosystems, while showcasing real‑world security and e‑commerce applications and outlining future hardware and algorithmic directions.

AlibabaGNN FrameworkGraph Neural Networks

0 likes · 15 min read

How Alibaba’s Open‑Source Graph‑Learn Accelerates GNN Deployment

DataFunTalk

Dec 30, 2019 · Artificial Intelligence

Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking

This article surveys recent advances in recommendation system technology, covering the evolution from a two‑stage recall‑ranking pipeline to a four‑stage architecture, and detailing emerging trends in model‑based recall, user‑behavior sequence modeling, knowledge‑graph integration, graph neural networks, advanced ranking models, multi‑objective optimization, multimodal fusion, and listwise re‑ranking.

Graph Neural NetworksInformation RetrievalMulti-Task Learning

0 likes · 45 min read

Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking

DataFunTalk

Dec 25, 2019 · Artificial Intelligence

Exploring Depth in Graph Convolutional Networks (GCN): Architecture, Experiments, and Future Work

This article examines the challenges of deepening Graph Convolutional Networks (GCN), introduces ResGCN, DenseGCN, and skip‑neighbor designs to enable deeper architectures, presents experimental results showing improved performance with 28‑layer models, and outlines future research directions.

Deep LearningGCNGraph Neural Networks

0 likes · 7 min read

Exploring Depth in Graph Convolutional Networks (GCN): Architecture, Experiments, and Future Work

Alibaba Cloud Developer

Dec 10, 2019 · Artificial Intelligence

Why GNNs Matter: Inside Alibaba’s AliGraph Platform for Scalable Graph AI

The article introduces AliGraph, Alibaba’s comprehensive Graph Neural Network platform showcased at NeurIPS 2019, explaining its layered architecture, scalable graph engine, extensible operators, and real‑world applications across e‑commerce, security and cloud services, while highlighting performance gains, supported algorithms, and the strategic focus on GNN research and development.

AI platformAlibabaGraph Neural Networks

0 likes · 14 min read

Why GNNs Matter: Inside Alibaba’s AliGraph Platform for Scalable Graph AI

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 FinancialGraph Neural NetworksLarge-Scale Graph

0 likes · 12 min read

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

Alibaba Cloud Developer

Aug 2, 2019 · Artificial Intelligence

Alibaba’s AI Breakthroughs at KDD 2019: From CTR Prediction to Graph Learning

This article summarizes Alibaba’s twelve KDD 2019 papers, covering advances in long‑sequence CTR modeling, fashion recommendation, sponsored search, exact‑K recommendation, meta‑learning, transfer learning, scalable graph convolution, heterogeneous graph neural networks, knowledge‑driven product description, and related workshops, highlighting both algorithmic innovations and industrial deployments.

.aiGraph Neural NetworksSparse Data

0 likes · 20 min read

Alibaba’s AI Breakthroughs at KDD 2019: From CTR Prediction to Graph Learning

DataFunTalk

Mar 27, 2019 · Artificial Intelligence

Understanding Graph Convolutional Networks through Heat Diffusion and Laplacian Operators

The article explains how the heat diffusion equation and the Laplacian operator on graphs provide a physical intuition for Graph Convolutional Networks, showing the equivalence between continuous‑space Fourier analysis and discrete‑space message passing, and linking these concepts to semi‑supervised learning and GraphSAGE implementations.

GCNGraph Neural NetworksLaplacian

0 likes · 19 min read

Understanding Graph Convolutional Networks through Heat Diffusion and Laplacian Operators

Hulu Beijing

Mar 14, 2019 · Artificial Intelligence

Unveiling Graph Neural Networks: Core Structures and Their Evolution

This article introduces the fundamental architecture of Graph Neural Networks, traces their evolution from early 2005 concepts through spatial and spectral models, explains why GNNs are gaining attention, and poses key questions about graph spectrum, Fourier transforms, and spectral‑domain graph convolutions.

Artificial IntelligenceGNNGraph Neural Networks

0 likes · 6 min read

Unveiling Graph Neural Networks: Core Structures and Their Evolution

Alibaba Cloud Developer

Feb 13, 2019 · Artificial Intelligence

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

This article explores how cognitive computing combined with graph neural networks and text generation enables large‑scale interest mining, interpretable embeddings, and multi‑modal recommendation in e‑commerce, outlining platform implementations, explainable methods, and future directions for AI‑driven consumer engagement.

Graph Neural NetworksRecommendation Systemscognitive computing

0 likes · 9 min read

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

iQIYI Technical Product Team

Feb 1, 2019 · Industry Insights

Will 2019 Be the Golden Harvest for AI? Insights from iQiyi’s VP

In a recent interview, iQiyi’s Vice President Xie Danming predicts that 2019 will mark a golden harvest for artificial intelligence, driven by 5G‑enabled AR/VR expansion, mature voice AI, emerging graph neural networks, and growing application‑focused investment, while highlighting challenges such as model cost, multimodal analysis, and the need for lightweight algorithms.

.ai5GAR/VR

0 likes · 15 min read

Will 2019 Be the Golden Harvest for AI? Insights from iQiyi’s VP

Alibaba Cloud Developer

Jan 18, 2019 · Artificial Intelligence

How Alibaba’s Open‑Source Euler Framework Powers Large‑Scale Graph Deep Learning

Euler, Alibaba's newly open‑sourced graph deep‑learning framework, combines distributed graph processing with neural network training to handle billions of nodes and edges, supports heterogeneous graphs, offers built‑in algorithms, and has already boosted advertising, fraud detection, and other industry applications.

AI InfrastructureDistributed ComputingEuler framework

0 likes · 11 min read

How Alibaba’s Open‑Source Euler Framework Powers Large‑Scale Graph Deep Learning

AntTech

Nov 1, 2018 · Artificial Intelligence

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

This article introduces the GEM method, the first heterogeneous graph neural network designed for malicious account detection, explains the nature and characteristics of malicious accounts, describes why graph neural networks are effective, and presents experimental results from the authors' CIKM 2018 study.

AI securityCIKM 2018Graph Neural Networks

0 likes · 8 min read

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

AntTech

Aug 22, 2018 · Artificial Intelligence

Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing

The article presents three Ant Financial research papers featured at KDD 2018—one on graph‑learning fraud detection for return‑freight insurance, another introducing the adaptive GeniePath graph neural network, and a third describing a distributed collaborative hashing system for large‑scale recommendation—highlighting their methodologies, experimental results, and practical impact on Ant Financial’s services.

Ant FinancialGraph Neural NetworksHashing

0 likes · 21 min read

Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing