HydroGraphNet boosts watershed predictions of daily flow and nitrogen in sparse data regions

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of ...
EurekAlert!

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of agricultural watersheds. To address this need, a team of researchers led by the ...
IEEE

Position-Aware Structural Knowledge Sharing-Based Federated Graph Learning for Intelligent Transportation Systems

Abstract: Federated Graph Learning (FGL) combines the powerful graph data modeling capabilities of Graph Neural Networks (GNNs) with the distributed processing requirements of intelligent ...
Search Engine Land

How to identify and fix AI hallucinations about your brand

AI hallucinations about a brand come in many types: A generative AI system might show a person who isn’t the actual founder, display the wrong address for the headquarters, or describe an old product ...
IEEE

When Knowledge Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study

Abstract: This paper investigates a GraphRAG framework that integrates knowledge graphs into the Retrieval-Augmented Generation (RAG) architecture to enhance networking applications. While RAG has ...
VentureBeat

Microsoft Fabric's graph gambit: How LinkedIn tech aims to solve AI's context problem

Credit: Image generated by VentureBeat with FLUX-pro-1.1 Without data, enterprise AI isn't going to be successful. Getting all the data in one place and having the right type of data tools, including ...
Forbes

Uncovering 'Dark Data' With AI-Driven Knowledge Graphs

A TechRadar article noted that nearly 90% of enterprise information (documents, emails, videos) lies dormant in unstructured systems. This "dark data" isn't just neglected; it's a liability. GenAI ...