Algorithmic graph theory and optimisation represents a critical nexus between discrete mathematics and computer science, underpinning the development of efficient methodologies for analysing complex ...
Nature: Complex brain networks: graph theoretical analysis of structural and functional systems
Understanding the network organization of the brain has been a long-standing challenge for neuroscience. In the past decade, developments in graph theory have provided many new methods for ...
Bootstrap percolation, a model of irreversible activation on graphs, has emerged as a pivotal area within graph theory and statistical mechanics. In this process, nodes (or vertices) on a network are ...
Graph theory isn’t enough. The mathematical language for talking about connections, which usually depends on networks—vertices (dots) and edges (lines connecting them)—has been an invaluable way to ...
For those who hear the phrase “graph theory” and think of the basic pie charts and bar graphs introduced in elementary school, there’s a new world to be explored. “In graph theory, the most simple way ...
Graph theory isn’t enough. The mathematical language for talking about connections, which usually depends on networks — vertices (dots) and edges (lines connecting them) — has been an invaluable way ...
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 ...
Giulia Livieri sets out remarkable new research with results that clarify how learning works on complex graphs and how quickly any method (including Graph Convolutional Networks) can learn from them, ...