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AI Large Model Application Practice
AI Large Model Application Practice
Feb 19, 2026 · Artificial Intelligence

When Should You Add a Knowledge Graph? 6 Practical Decision Criteria

This article outlines six concrete criteria—relationship‑centric data, reproducible reasoning, evolving schemas, multi‑hop queries, explainable decisions, and cross‑system data integration—to help engineers decide whether a knowledge graph is the right solution or if a relational database will suffice.

AI EngineeringData Integrationexplainability
0 likes · 15 min read
When Should You Add a Knowledge Graph? 6 Practical Decision Criteria
Architects Research Society
Architects Research Society
Sep 10, 2025 · Artificial Intelligence

From Vectors to Graphs to Hybrids: The Evolution of AI Knowledge Representation

This article explores the three stages of AI knowledge representation—vector embeddings, graph‑based structures, and the emerging hybrid approach that combines vectors, graphs, and large language models—to illustrate how modern Retrieval‑Augmented Generation systems achieve both semantic similarity and precise relational reasoning.

AIRetrieval Augmented Generationgraph databases
0 likes · 3 min read
From Vectors to Graphs to Hybrids: The Evolution of AI Knowledge Representation
DataFunSummit
DataFunSummit
Nov 9, 2024 · Artificial Intelligence

GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments

This article introduces GraphRAG, explains the limitations of traditional RAG, outlines four major challenges (fine‑grained retrieval, global context, similarity vs relevance, and macro‑level reasoning), describes GraphRAG’s graph‑based retrieval strategies, showcases comparative experiments, and presents NebulaGraph’s GenAI Suite and RAG products along with future research directions.

AIGraphRAGLarge Language Models
0 likes · 16 min read
GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments
DataFunSummit
DataFunSummit
Aug 4, 2024 · Artificial Intelligence

Graph Technology Overview and Applications – From GraphGPT to Graph Databases

This article presents a comprehensive overview of recent advances in graph technology, covering GraphGPT for large language models, knowledge transfer on complex graphs, financial fraud detection, telecom network optimization, graph foundation models, Baidu's multi‑domain recommendation, high‑availability graph databases, and Kuaishou's efficient recommendation architecture.

Large Language ModelsRecommendation Systemsfinancial fraud detection
0 likes · 4 min read
Graph Technology Overview and Applications – From GraphGPT to Graph Databases
DataFunSummit
DataFunSummit
Jun 9, 2024 · Artificial Intelligence

Graph Technology and Graph Learning in Telecom Networks: Development, Applications, and Performance Optimization

This article reviews the evolution of graph technology, its applications in telecom, finance, and recommendation systems, discusses challenges of storage and querying large-scale graphs, and presents performance‑optimizing techniques for graph learning engines such as Wind.

graph databasesgraph learningtelecom networks
0 likes · 30 min read
Graph Technology and Graph Learning in Telecom Networks: Development, Applications, and Performance Optimization
AntTech
AntTech
Sep 21, 2022 · Big Data

2022 China Graph Computing Technology and Application Development Research Report Overview

China’s graph computing landscape is rapidly evolving, with the 2022 CB Insights report detailing the technology’s shift from traditional relational databases to graph databases and engines, highlighting industry growth, academic research surges, major investments, open‑source initiatives, and diverse applications across finance, energy, and AI.

AIIndustry Analysisgraph databases
0 likes · 14 min read
2022 China Graph Computing Technology and Application Development Research Report Overview
AntTech
AntTech
Sep 2, 2022 · Databases

Highlights of the 2022 World AI Conference Graph Intelligence Forum in Shanghai

The September 1, 2022 Graph Intelligence Forum at the World AI Conference in Shanghai gathered leading researchers and industry experts to discuss advances in graph computing, learning, and databases, announced the open‑source TuGraph as the world’s fastest graph database, and emphasized standards, temporal graphs, and industry‑academia collaboration.

AILDBCTuGraph
0 likes · 7 min read
Highlights of the 2022 World AI Conference Graph Intelligence Forum in Shanghai
AntTech
AntTech
Aug 28, 2022 · Databases

Insights from the Beijing Graph Computing Seminar: Industry‑Academia Collaboration and Future Directions

The Beijing seminar co‑hosted by MIT Technology Review China and Ant Group highlighted the rapid rise of graph computing, discussed academic‑industry cooperation models, explored research hotspots such as distributed graph platforms and AI‑driven graph pre‑training, and examined practical challenges and future prospects for graph databases across sectors.

Artificial IntelligenceData AnalyticsIndustry-Academia Collaboration
0 likes · 13 min read
Insights from the Beijing Graph Computing Seminar: Industry‑Academia Collaboration and Future Directions
DataFunSummit
DataFunSummit
Apr 24, 2022 · Databases

Subgraph Matching in Graph Databases: Concepts, Algorithms, and Optimizations

This article introduces graph databases, contrasts them with relational databases, explains the subgraph‑matching problem and its computational complexity, surveys backtracking and multi‑way join algorithms, discusses worst‑case‑optimal joins, set‑intersection acceleration, hardware support, and presents PKUMOD’s gStore research and its distributed extensions.

SPARQLgStoregraph databases
0 likes · 19 min read
Subgraph Matching in Graph Databases: Concepts, Algorithms, and Optimizations
DataFunTalk
DataFunTalk
Apr 18, 2022 · Databases

Subgraph Matching in Graph Databases: Concepts, Algorithms, and Optimizations

This article introduces graph databases, explains the subgraph‑matching problem, compares it with relational databases, discusses its computational complexity, and surveys backtracking and multi‑way join algorithms, worst‑case optimal joins, set‑intersection SIMD acceleration, and the gStore system’s research contributions.

RDFSIMDSPARQL
0 likes · 19 min read
Subgraph Matching in Graph Databases: Concepts, Algorithms, and Optimizations
DataFunTalk
DataFunTalk
Dec 27, 2021 · Databases

Graph Theory, Graph Databases, and the Graph Intelligent Platform: Concepts, Development, and Tencent Use Cases

This article explores the fundamentals and evolution of graph theory, graph databases, and graph computing, discusses Tencent's self‑built graph stack—including EasyGraph, Angel‑Graph, and visualization tools—and demonstrates real‑world applications such as scheduling, financial payment analysis, and fraud detection, highlighting performance gains and future trends.

Graph VisualizationTencentfraud detection
0 likes · 17 min read
Graph Theory, Graph Databases, and the Graph Intelligent Platform: Concepts, Development, and Tencent Use Cases
AntTech
AntTech
Dec 23, 2021 · Databases

Understanding Graph Computing: Fundamentals, Applications, and Future Directions

This article explains graph computing fundamentals, illustrates its use in fraud detection, search ranking, and brain modeling, highlights Ant Group's record‑breaking performance and standards efforts, and outlines future challenges such as standardization, higher performance, and integration with AI.

Artificial IntelligenceBig Datagraph computing
0 likes · 13 min read
Understanding Graph Computing: Fundamentals, Applications, and Future Directions
DataFunSummit
DataFunSummit
Dec 8, 2021 · Artificial Intelligence

Knowledge Graph Forum at DataFunCon – Speakers, Topics, and Registration Details

The DataFunCon Knowledge Graph Forum on December 18 gathers leading experts from academia and industry to discuss large‑scale knowledge graph construction, storage, applications, and challenges, offering attendees insights into cutting‑edge AI techniques, graph databases, and practical deployment strategies across multiple domains.

Artificial Intelligencegraph databasesindustry applications
0 likes · 11 min read
Knowledge Graph Forum at DataFunCon – Speakers, Topics, and Registration Details
DataFunTalk
DataFunTalk
Oct 8, 2021 · Artificial Intelligence

Graph Computing for Financial Credit Risk Control: Architecture, Challenges, and Lessons Learned

This article explores how graph computing is applied to financial credit risk and anti‑fraud, detailing the business background, terminology, stakeholder roles, system requirements, architectural evolution across three phases, practical challenges, and key take‑aways for building stable, timely, accurate, and controllable graph‑based risk models.

AIfinancial riskfraud detection
0 likes · 14 min read
Graph Computing for Financial Credit Risk Control: Architecture, Challenges, and Lessons Learned
DataFunSummit
DataFunSummit
Oct 8, 2021 · Artificial Intelligence

Graph Computing for Financial Credit Risk Control and Anti‑Fraud: Architecture, Challenges, and Lessons Learned

This article examines how graph computing is applied to financial credit risk management and anti‑fraud, covering business background, key credit terminology, stakeholder roles, graph‑based fraud detection techniques, system architecture evolution across three development stages, practical requirements such as stability, timeliness, accuracy and controllability, and summarizes operational insights.

AIanti-fraudgraph computing
0 likes · 16 min read
Graph Computing for Financial Credit Risk Control and Anti‑Fraud: Architecture, Challenges, and Lessons Learned
DataFunTalk
DataFunTalk
May 21, 2021 · Artificial Intelligence

Knowledge Graph Course Syllabus Overview

This document outlines a comprehensive knowledge graph curriculum covering fundamentals, representation methods, storage solutions, extraction techniques, reasoning, fusion, question answering, graph algorithms, and emerging research directions across nine detailed chapters.

AI educationcourse syllabusgraph databases
0 likes · 4 min read
Knowledge Graph Course Syllabus Overview
Architects Research Society
Architects Research Society
Jul 14, 2020 · Frontend Development

Graph Visualization Ecosystem: Overview of Libraries, Toolkits, and Applications

This article, the final part of the GraphTech ecosystem series, provides a comprehensive overview of the front‑end graph visualization layer, detailing its role, benefits, and a curated list of over 70 open‑source and commercial libraries, toolkits, software, and built‑in visualizers for graph data analysis.

Graph Visualizationdata analysisfrontend libraries
0 likes · 9 min read
Graph Visualization Ecosystem: Overview of Libraries, Toolkits, and Applications
21CTO
21CTO
Feb 12, 2019 · Databases

From MariaDB CTO to Neo4j: Ivan Zoratti’s Journey into Graph Databases

Former MariaDB CTO Ivan Zoratti shares his 35‑year tech career, the evolution from MySQL to MariaDB, why he transitioned to Neo4j, and his insights on graph versus relational databases, highlighting the innovations and future potential of graph technology.

MariaDBNeo4jRelational Databases
0 likes · 12 min read
From MariaDB CTO to Neo4j: Ivan Zoratti’s Journey into Graph Databases