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Graph Machine Learning

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DataFunSummit
DataFunSummit
Mar 21, 2023 · Artificial Intelligence

Interview with Huawei Noah's Ark Lab Senior Researcher Zhou Min on Graph Machine Learning: Research, Deployment, Challenges, and Trends

In this DataFun interview, Huawei Noah's Ark Lab senior researcher Zhou Min discusses the state of graph machine learning in academia and industry, covering algorithmic foundations, model variants, practical applications, scalability challenges, and future directions for more universal feature extraction across domains.

AI researchDataFunGraph Machine Learning
0 likes · 9 min read
Interview with Huawei Noah's Ark Lab Senior Researcher Zhou Min on Graph Machine Learning: Research, Deployment, Challenges, and Trends
DataFunTalk
DataFunTalk
Sep 2, 2022 · Artificial Intelligence

BDTC 2022 China Big Data Technology Conference – Graph Machine Learning Forum (September 3, 2022)

The BDTC 2022 China Big Data Technology Conference, held on September 3 at Fourth Paradigm Technology Co., showcases a Graph Machine Learning Forum featuring leading academics and industry experts who present cutting‑edge research, scalable graph neural network systems, financial fraud defense, and autonomous knowledge‑graph learning, with detailed speaker bios, talk titles, abstracts, and registration information.

Artificial IntelligenceBig DataConference
0 likes · 11 min read
BDTC 2022 China Big Data Technology Conference – Graph Machine Learning Forum (September 3, 2022)
DataFunSummit
DataFunSummit
May 23, 2022 · Artificial Intelligence

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

This article explores how graph machine learning can be leveraged for intelligent anti‑fraud, covering business background, common fraud models and graph algorithm principles, practical deployment of graph algorithms, challenges in fraud modeling, and future research directions.

Graph Machine Learningfraud detectiongraph algorithms
0 likes · 20 min read
Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications