Machine learning models are often drowning in data, but the problem is not always the sheer volume of samples. Increasingly, ...

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 ...

Graphs are everywhere. Whenever a delivery company models a road network, a power grid operator tracks the flow of ...

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 ...

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of ...

Graph colouring, the assignment of colours to the vertices of a graph so that no two adjacent vertices share the same colour, represents a canonical NP-hard combinatorial optimisation problem with ...

This book offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based ...

StellarGraph has launched a series of new algorithms for network graph analysis to help discover patterns in data, work with larger data sets and speed up performance while reducing memory usage.

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 ...

Computer scientists are abuzz over a fast new algorithm for solving one of the central problems in the field. (January 15, 2017, update: On January 4, Babai retracted his claim that the new algorithm ...