Graph algorithms and combinatorial optimisation form a pivotal area of research that underpins many modern computational applications. At their core, graph algorithms provide systematic methods for ...

The graph colouring problem, a classic NP-hard challenge, is central to many practical applications such as scheduling, resource allocation and network management. Recent advances have seen the ...

Motif-based graph local clustering is a popular method for graph mining tasks due to its various applications, such as community detection, network optimization and graph learning. However, the ...

Discover what context graphs are, why they're revolutionizing AI systems, and who's building this trillion-dollar technology ...

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 ...

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 ...

Back in the hazy olden days of the pre-2000s, navigating between two locations generally required someone to whip out a paper map and painstakingly figure out the most optimal route between those ...

Quantum walks sound abstract, but they sit at the center of a very concrete race: who will harness quantum mechanics to solve ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. In this episode, Thomas Betts chats with ...

The demo program in Figure 1 begins by setting up nine parameters that are used by the EO algorithm. Compared to other optimization techniques, EO requires many free parameters (this is considered a ...