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 ...

Graph enumeration in complex networks encompasses a suite of methods designed to count and characterise substructures such as spanning trees, motifs and subgraphs, offering insights into network ...

Abstract: This paper addresses the problem of learning an undirected graph from data gathered at each node. Within Gaussian graphical models (GGM), the topology of such graph can be linked to the ...

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 ...

“Graph DB for agentic AI” FalkorDB—Unlike some traditional graph stores, FalkorDB represents adjacency using sparse matrices and leverages linear-algebra ops (GraphBLAS under the hood) for queries, ...