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 ...
InfoQ

InfoQ Java Trends Report 2025

AI on the JVM accelerates: New frameworks like Embabel, Koog, Spring AI, and LangChain4j drive rapid adoption of AI-native and AI-assisted development in Java. Java 25 anchors a modern baseline: The ...
PNAS

Community detection for directed networks revisited using bimodularity

The art of finding patterns or communities plays a central role in the analysis of structured data such as networks. Community detection in graphs has become a field on its own. Real-world networks, ...
Quanta Magazine

New Method Is the Fastest Way To Find the Best Routes

If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle the easiest pieces first. But this kind of sorting has a cost.
C&EN

Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein–Ligand Binding Affinity Prediction

Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States ...
marktechpost

Neural SpaceTimes (NSTs): A Class of Trainable Deep Learning-based Geometries that can Universally Represent Nodes in Weighted Directed Acyclic Graphs (DAGs) as Events in a ...

Directed graphs are crucial in modeling complex real-world systems, from gene regulatory networks and flow networks to stochastic processes and graph metanetworks. Representing these directed graphs ...
acm.org

Technical Perspective: Shortening the Path to Designing Efficient Graph Algorithms

Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to ...