graph embedding

Baidu Geek Talk

Dec 18, 2024 · Artificial Intelligence

GEE Graph Embedding Algorithm for Business Security Anomaly Detection

The article presents the GEE (Graph Encoder Embedding) algorithm for business security anomaly detection, explains its label‑propagation foundation, evaluates it on ten‑million‑edge real data, identifies inefficiencies in the original implementation, and demonstrates that vectorized NumPy/Pandas optimizations reduce runtime from 55 seconds to about 4 seconds while preserving meaningful TSNE‑visualized embeddings.

Anomaly DetectionGEE algorithmPerformance Optimization

0 likes · 21 min read

GEE Graph Embedding Algorithm for Business Security Anomaly Detection

Tencent Cloud Developer

May 29, 2024 · Artificial Intelligence

Distributed Network Embedding Algorithm for Billion‑Scale Graph Data in Tencent Games

Tencent’s Game Social Algorithm Team presents a Spark‑based distributed network embedding framework that recursively partitions hundred‑billion‑edge game graphs into manageable subgraphs, runs node2vec locally, and fuses results, enabling efficient link prediction and node classification across multiple games within hours.

Big DataSparkdistributed computing

0 likes · 7 min read

Distributed Network Embedding Algorithm for Billion‑Scale Graph Data in Tencent Games

DataFunTalk

Jan 11, 2024 · Artificial Intelligence

Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution

This article introduces the use of graph models in Baidu's recommendation system, covering graph fundamentals, common graph algorithms such as graph embedding and graph neural networks, the evolution of the Feed graph model, and its subsequent promotion across multiple product lines.

BaiduGraph Neural Networksgraph embedding

0 likes · 10 min read

Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution

Architect

Jun 10, 2023 · Artificial Intelligence

An Overview of Twitter’s Open‑Source Recommendation System Architecture

Twitter’s recently open‑sourced recommendation system is dissected, covering its overall architecture, graph‑based data and feature engineering, recall pipelines (in‑network and out‑of‑network), coarse and fine ranking models, mixing and re‑ranking stages, as well as the supporting infrastructure and code examples.

Twittergraph embeddingmachine learning

0 likes · 16 min read

An Overview of Twitter’s Open‑Source Recommendation System Architecture

Tongcheng Travel Technology Center

May 9, 2023 · Artificial Intelligence

Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation

This article presents EGES, a graph‑embedding model that incorporates side information to construct a directed user graph, apply biased random‑walk sampling, and train weighted Skip‑Gram embeddings, thereby improving large‑scale user acquisition and addressing cold‑start challenges in recommendation systems.

Cold StartEGESgraph embedding

0 likes · 9 min read

Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation

DataFunSummit

Mar 16, 2023 · Artificial Intelligence

Construction of Real‑World Medical Knowledge Graphs and Clinical Event Graphs

The article describes how YiduCloud builds real‑world medical knowledge graphs and clinical event graphs from heterogeneous hospital systems (EMR, HIS, LIS, RIS) using data aggregation, de‑identification, quality control, NLP‑driven entity extraction, standardisation, graph construction, cleaning, embedding and various AI‑powered applications such as decision support, intelligent diagnosis, automated medical‑record generation and patient recruitment.

AIBig DataMedical Knowledge Graph

0 likes · 21 min read

Construction of Real‑World Medical Knowledge Graphs and Clinical Event Graphs

DataFunTalk

Oct 3, 2022 · Artificial Intelligence

Building Real‑World Medical Knowledge Graphs and Clinical Event Graphs: Methods, Pipelines, and Applications

This article explains how YiduCore processes heterogeneous hospital data (EMR, HIS, LIS, RIS, literature) to construct real‑world medical knowledge graphs and clinical event graphs, detailing pipelines for entity extraction, normalization, graph cleaning, PSR scoring, graph embedding, and showcasing applications such as intelligent diagnosis, question answering, automated medical record generation, and clinical trial patient recruitment.

AIBig DataMedical Knowledge Graph

0 likes · 21 min read

Building Real‑World Medical Knowledge Graphs and Clinical Event Graphs: Methods, Pipelines, and Applications

Zhuanzhuan Tech

Sep 14, 2022 · Artificial Intelligence

Graph Embedding Algorithms and Their Application in Zhuanzhuan Recommendation System

This article introduces the fundamentals of recommendation systems, explains Zhuanzhuan's main recommendation scenarios and pipeline, and details three graph embedding methods—DeepWalk, node2vec, and EGES—along with their practical implementations in recall and coarse‑ranking stages.

DeepWalkEGESe-commerce

0 likes · 17 min read

Graph Embedding Algorithms and Their Application in Zhuanzhuan Recommendation System

DataFunSummit

Jul 10, 2022 · Artificial Intelligence

Intelligent Industry Analysis Tool Based on Knowledge Graphs and Industry Atoms

This article introduces VentureSights, an AI‑driven intelligent industry analysis platform built on knowledge‑graph technology and the concept of industry atoms, detailing its core modules, workflow, industry‑atom representation, extraction algorithms, and overall system architecture for generating comprehensive industry reports and insights.

Artificial IntelligenceBig DataIndustry Analysis

0 likes · 12 min read

Intelligent Industry Analysis Tool Based on Knowledge Graphs and Industry Atoms

HelloTech

May 26, 2022 · Artificial Intelligence

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

Hello’s automated C‑side growth algorithm loop integrates diverse traffic sources, semi‑supervised PU‑learning, graph‑based look‑alike targeting, causal uplift models for smart subsidies, and adaptive copy and external ad optimization, dramatically boosting ride‑hailing and lifestyle service revenue while minimizing engineering duplication.

AI PlatformRecommendation systemsgraph embedding

0 likes · 20 min read

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

DataFunSummit

Apr 17, 2022 · Artificial Intelligence

Precise Marketing Algorithms and Practices at Hello Mobility

This article presents Hello Mobility’s precise marketing system, covering its business background, value, framework, algorithmic capabilities such as Pu‑Learning LookAlike modeling, TSA semi‑supervised learning, and Graph Embedding, as well as identified pain points, project impact, and future directions for scaling and automation.

AIPrecise MarketingSemi-supervised Learning

0 likes · 12 min read

Precise Marketing Algorithms and Practices at Hello Mobility

HelloTech

Apr 11, 2022 · Artificial Intelligence

Precise Marketing Algorithm and Practice in HaLao

HaLao’s precise marketing system combines Pu‑Learning lookalike models, graph‑embedding user similarity analysis, and TSA/EM optimization within a robust feature‑engineering and deployment framework, delivering over 20% ROI gains and 3‑10× user growth while addressing low‑ROI and targeting inefficiencies.

AI in marketingMarketing Algorithmsdata analysis

0 likes · 9 min read

Precise Marketing Algorithm and Practice in HaLao

DataFunTalk

Apr 11, 2022 · Artificial Intelligence

Precise Marketing Algorithms and Practices at Hello Mobility

This article presents Hello Mobility's precise marketing system, detailing its business background, value, framework, algorithmic capabilities—including Pu‑Learning LookAlike modeling, semi‑supervised TSA, and graph‑embedding techniques—addressing challenges such as sparse features and low ROI, and sharing performance improvements and future directions.

Data EngineeringPrecise MarketingPu-Learning

0 likes · 14 min read

Baidu Intelligent Testing

Sep 14, 2021 · Information Security

Community Encoding Based Detection of Black and Gray Market Attacks Using Graph Embedding

This article presents a community‑encoding approach that leverages large‑scale graph‑embedding (GraphSAGE) and asynchronous near‑real‑time engineering to identify and measure unknown black‑gray market attacks with higher accuracy and flexibility than traditional graph‑mining methods.

GraphSAGEblack‑gray marketcommunity-detection

0 likes · 15 min read

Community Encoding Based Detection of Black and Gray Market Attacks Using Graph Embedding

DataFunTalk

Jun 23, 2021 · Artificial Intelligence

Graph Algorithm Practices for Anti‑Cheat on the Douyu Live‑Streaming Platform

This article explains how Douyu uses graph‑based algorithms to detect and mitigate fraudulent streaming traffic, covering the platform's risk‑control scenarios, the overall graph architecture, its evolution, modeling workflow, practical case studies, and the resulting improvements in detection accuracy and interpretability.

Live Streaminganti-cheatgraph algorithms

0 likes · 16 min read

Graph Algorithm Practices for Anti‑Cheat on the Douyu Live‑Streaming Platform

Baidu Geek Talk

Jun 23, 2021 · Information Security

Black-Gray Industry Attack Detection Based on Community Encoding Using Graph Embedding

The paper introduces a community‑encoding, GraphSAGE‑based detection framework that embeds whole user‑account, IP, device, and phone‑number graphs—both homogeneous and heterogeneous—to identify previously unseen black‑gray industry attacks, achieving about 95% IP‑risk accuracy via an asynchronous near‑real‑time system, though computational and automation challenges persist.

GraphSAGEblack-gray-industrycommunity-detection

0 likes · 12 min read

Black-Gray Industry Attack Detection Based on Community Encoding Using Graph Embedding

Tencent Cloud Developer

Jun 9, 2021 · Artificial Intelligence

Overview of Common Graph Embedding Methods in Industry

The article surveys six widely‑used graph‑embedding techniques—DeepWalk, Node2Vec, LINE, SDNE, EGES and Metapath2Vec—explaining how each transforms graph topology into low‑dimensional vectors via random walks, biased sampling, proximity‑based objectives, deep auto‑encoders, side‑information integration, or meta‑path‑guided walks for industrial applications.

DeepWalkEGESMetapath2Vec

0 likes · 14 min read

Overview of Common Graph Embedding Methods in Industry

DataFunTalk

Feb 25, 2021 · Artificial Intelligence

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

This article describes how a UGC app tackled user and content cold‑start problems by introducing a personalized vector‑recall pipeline based on network representation learning and multimodal embeddings, detailing graph construction, GraphSAGE and GAT implementations, offline experiments, A/B test results, and future directions.

GNNgraph embeddingmultimodal

0 likes · 14 min read

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

DataFunTalk

Jan 1, 2021 · Artificial Intelligence

Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems

This article surveys recent research on extracting and expanding hot topics from short texts by constructing user‑behavior graphs, applying graph‑embedding techniques, and leveraging multi‑task learning to improve recommendation relevance, timeliness, and cold‑start handling in large‑scale platforms.

Artificial IntelligenceRecommendation systemsgraph embedding

0 likes · 12 min read

Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems

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 Networksdeep learning

0 likes · 11 min read

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