One powerful way to do this is through a routine called slow reveal graphs.

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, ...

Developed during ten years of teaching experience, this book serves as a set of lecture notes for an introductory course on numerical computation, at the senior undergraduate level. These notes ...

The OTT Parsivel2 is a laser-optical disdrometer that measures the size and velocity of hydrometeors (Loffler-Mang and Joss, 2000). Usually the sampling output is 1 min, in a text file. The Parsivel2 ...

Abstract: Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been human ...

You can use these live scripts as demonstrations in lectures, class activities, or interactive assignments outside class. In the first script, students learn to derive transfer functions from ODEs and ...

Abstract: Recently, many works on Graph Neural Networks (GNNs) have been well developed for graph-level representation learning tasks and continuously improve graph classification accuracy. These ...

This section focuses on the key features and methods for working with linear graphs. It demonstrates how to sketch graphs from rules, derive rules from graphs, and calculate key features such as the ...