Graph labeling is a central topic in combinatorial optimisation that involves assigning numerical or categorical labels to vertices or edges of a graph subject to specific constraints. This framework ...

In recent years, the Massively Parallel Computation (MPC) model has gained significant attention. However, most of distributed and parallel graph algorithms in the MPC model are designed for static ...

Betweenness centrality is a fundamental metric in network science that quantifies the importance of a node by measuring the proportion of shortest paths that pass through it. This measure underpins ...

In algorithms, as in life, negativity can be a drag. Consider the problem of finding the shortest path between two points on a graph — a network of nodes connected by links, or edges. Often, these ...

A puzzle that has long flummoxed computers and the scientists who program them has suddenly become far more manageable. A new algorithm efficiently solves the graph isomorphism problem, computer ...

Academics from the Department’s rapidly growing Algorithms Group are celebrating having three papers accepted for the 63rd Institute of Electrical and Electronics Engineers (IEEE) Annual Symposium on ...

Two computer scientists found — in the unlikeliest of places — just the idea they needed to make a big leap in graph theory. This past October, as Jacob Holm and Eva Rotenberg were thumbing through a ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...