Tagged articles

Graph Modeling

9 articles · Page 1 of 1

Aug 4, 2026 · Artificial Intelligence

NaviAgent: Scalable Tool Orchestration for Oxygen Agents via Graph‑Driven Bilevel Planning

The paper introduces NaviAgent, a double‑layer architecture that separates LLM‑based planning from graph‑driven tool navigation, explicitly models API‑parameter dependencies, continuously updates the tool graph with execution feedback, and achieves up to 13.1 % higher task success rates on large‑scale API benchmarks.

AI agentsDynamic PlanningGraph Modeling

0 likes · 16 min read

NaviAgent: Scalable Tool Orchestration for Oxygen Agents via Graph‑Driven Bilevel Planning

Data Integration and Governance

Mar 24, 2026 · Fundamentals

Four Core Data Modeling Techniques Every Engineer Should Know

The article explains why solid data modeling is essential, then walks through four widely used techniques—normalization, dimensional modeling, Data Vault, and graph modeling—detailing their principles, typical use cases, advantages, and trade‑offs, and shows how they fit into layered data‑warehouse architectures.

Data ModelingData VaultData Warehouse

0 likes · 10 min read

Four Core Data Modeling Techniques Every Engineer Should Know

Alibaba Cloud Developer

Jan 12, 2026 · Operations

Why Traditional Monitoring Fails and How UModel Redefines Observability for AI‑Powered Ops

The article explains how legacy monitoring based on isolated metrics, traces, and logs cannot keep up with the massive, fragmented, and dynamic data of modern IT systems, and introduces UModel—a graph‑based observability model that bridges data, model, and engineering gaps to enable AI‑driven operations.

AIOpsData ModelingGraph Modeling

0 likes · 11 min read

Why Traditional Monitoring Fails and How UModel Redefines Observability for AI‑Powered Ops

Alibaba Cloud Native

Jan 3, 2026 · Operations

Turning Chaotic Observability Data into Actionable Graphs with UModel

This article examines the evolution of IT observability, explains why traditional metrics, traces, and logs fall short for AI‑driven operations, and introduces UModel—a graph‑based universal observability model that structures fragmented data into a semantic runtime context for autonomous AIOps agents.

AIOpsCloud NativeGraph Modeling

0 likes · 12 min read

Turning Chaotic Observability Data into Actionable Graphs with UModel

Amap Tech

Oct 3, 2025 · Artificial Intelligence

How FantasyHSI Enables Autonomous 3D Human Interaction in Any Scene

FantasyHSI introduces a graph‑based multi‑agent framework that combines visual‑language models and video‑generation diffusion to let digital humans perceive, plan, and interact autonomously in any 3D scene, producing physically plausible, long‑duration actions for animation creation and embodied‑AI simulation.

3D synthesisGraph ModelingVideo Generation

0 likes · 12 min read

How FantasyHSI Enables Autonomous 3D Human Interaction in Any Scene

DataFunTalk

Nov 21, 2022 · Artificial Intelligence

Research on Information Extraction from a Graph Perspective

This presentation reviews the background, significance, current research status, objectives, and key contributions of a graph‑based approach to information extraction, covering entity recognition, relation extraction, event extraction, open‑domain extraction, and the proposed unified modeling framework with experimental results.

Graph ModelingNLPentity recognition

0 likes · 27 min read

Research on Information Extraction from a Graph Perspective

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.

GCNGraph ModelingReal-time inference

0 likes · 26 min read

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

Meituan Technology Team

Aug 20, 2020 · Artificial Intelligence

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.

AdvertisingBias MitigationGraph Modeling

0 likes · 23 min read

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

Youku Technology

Aug 4, 2020 · Operations

How Youku Scales User Reach: Inside the Architecture of a Billion‑User Messaging Platform

This talk reveals how Youku built a flexible, universal user‑reach platform that leverages graph‑based task modeling, dynamic expressions, and a unified execution engine to deliver recall and activation campaigns to over a billion users with fast, precise targeting and experiment‑driven optimization.

Execution EngineGraph ModelingLarge-Scale Operations

0 likes · 3 min read

How Youku Scales User Reach: Inside the Architecture of a Billion‑User Messaging Platform