Graphs are everywhere. In discrete mathematics, they are structures that show the connections between points, much like a public transportation network. Mathematicians have long sought to develop ...

Jacob Holm was flipping through proofs from an October 2019 research paper he and colleague Eva Rotenberg—an associate professor in the department of applied mathematics and computer science at the ...

The metric dimension is a key invariant in graph theory that encapsulates the minimal number of reference points, or “resolving sets”, required to uniquely determine the position of each vertex within ...

Algorithmic graph theory and optimisation represents a critical nexus between discrete mathematics and computer science, underpinning the development of efficient methodologies for analysing complex ...

Researchers thought that they were five years away from solving a math riddle from the 1980's. In reality, and without knowing, they had nearly cracked the problem and had just given away much of the ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

In the race toward practical quantum computers and networks, photons—fundamental particles of light—hold intriguing possibilities as fast carriers of information at room temperature. Researchers at ...

DPABINet, a sophisticated enhancement of the DPABI software suite, streamlines the intricate analysis of brain networks through fMRI data, providing researchers of all expertise levels with ...