This article explains the need for graph lakehouses, defines the concept, details Flavius’ cloud‑native three‑layer architecture (FE, BE, MS), highlights its core innovations such as resource management, metadata design, time‑travel, integrated graph compute and training, and showcases real‑world industry applications.
This article presents Fabarta's application of graph‑based artificial intelligence to anti‑money‑laundering, detailing the business challenges, a five‑step analytical workflow, a synthetic data case study, and a Q&A that explores practical deployment and future directions.
This article introduces graph databases and explains why they are suited for relationship‑centric data, then details TuGraph’s key characteristics, architecture layers, supported query languages, analytics capabilities, market trends, roadmap plans, target users, and community resources, including a free cloud trial.
This article introduces the fundamentals of knowledge graphs, explores their research dimensions—including knowledge engineering, NLP, databases, and machine learning—examines graph database storage models, discusses their relevance to AI and big data, and showcases the authors' own graph‑based projects and case studies.
This article introduces the basic concepts of knowledge graphs, explores their research dimensions across knowledge engineering, natural language processing, databases and machine learning, discusses graph database storage models and their integration with artificial intelligence and big data, and presents related projects and real‑world case studies.
This article presents a comprehensive case study on constructing an enterprise relationship knowledge graph for financial technology, detailing data preparation, ontology modeling, graph building, and three practical applications—path query, ultimate controller discovery, and group detection—demonstrating how AI-driven graph analytics can uncover hidden corporate connections.
This article examines the rapid growth of financing demand among small‑and‑micro enterprises in logistics and commercial vehicle sectors, outlines the high financial penetration in the industry, and details how AI‑driven intelligent risk‑control frameworks—covering data pipelines, model selection, feature‑portrait systems, and graph‑based applications—address the challenges and opportunities of modern financial risk management.
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.
The article details how Beike's Agent Cooperation Network employs a multi‑layered risk control framework built on large‑scale knowledge graphs, graph mining, and machine‑learning techniques to detect fake listings, malicious competition, and other threats across both online and offline real‑estate scenarios.
This article explains how machine learning, deep learning, and graph‑based techniques are applied to credit anti‑fraud in finance, covering fraud risk characteristics, the anti‑fraud lifecycle, rule limitations, supervised models, common algorithms, neural networks, time‑series models, and graph analytics for detecting individual and group fraud.
