Magnetic graph matrices are powerful tools for modeling quantum systems and directed networks, but their application in network analysis has been limited by a lack of combinatorial understanding. We ...

"Graphs are the natural language of relationships. Every social network, every molecule, every knowledge base, and every computation graph in a neural network is a graph — and the mathematics of ...

Topological indices are important numerical invariants that capture structural properties of algebraic graphs, similar to their use in chemical graph theory. In this context, an algebraic structure ...

Abstract: Accurate short-term load forecasting (STLF) requires capturing complex spatio-temporal dependencies, a task where standard Graph Neural Networks (GNNs) struggle due to static graph ...

In the dynamic realm of data representation, "time series graph examples" stand as stalwarts, offering a fresh perspective into the evolution of variables over time. As a data analyst of your company, ...