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 ...

How to Use the Kalshi App: Understanding Prediction Market Data Before Your First Trade

Prediction markets are the future, they aren’t just having their moment. Kalshi has grown into one of the most talked-about platforms in sports and financial me ...
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 ...
Search Engine Land

Entity-first SEO: How to align content with Google’s Knowledge Graph

For years, SEOs optimized pages around keywords. But Google now understands meaning through entities and how they relate to one another: people, products, concepts, and their topical connections ...
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 ...