A new open-access study reports that fusing attribute features with graph structure and training classifiers collaboratively ...

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 ...

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 ...

Researchers have introduced distributed algorithms that detect communities in a class of networks where time is built ...

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 ...

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 ...

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 ...

This research introduces quantum graph networks for max-clique detection, overcoming trainability issues with compositional ...

Most of you have used a navigation app like Google Maps for your travels at some point. These apps rely on algorithms that compute shortest paths through vast networks. Now imagine scaling that task ...