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 ...

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 ...

A professor has helped create a powerful new algorithm that uncovers hidden patterns in complex networks, with potential uses in fraud detection, biology and knowledge discovery. University of ...

It might not be as bright and shiny as some of the other topics that we've seen here, but there's no denying that the work of Julian Shun and his team is going to be applicable to a lot of the ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now DeepMind wants to enable neural networks to ...

A team of researchers at ETH Zurich are working on a novel approach to solving increasingly large graph problems. Large graphs are a basis of many problems in social sciences (e.g., studying human ...

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 ...

Like the core algorithm, Google’s Knowledge Graph periodically updates. But little has been known about how, when, and what it means — until now. I believe these updates consist of three things: ...

Graph databases, which explicitly express the connections between nodes, are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. That ...