Combined with algorithms that can handle such graphs it’s a great way to not only make the basic structure of a network clear, but also to model structures and systems.

And compute power has increased a trillion-fold, which makes it easier to recreate these algorithms at scale. Knowledge graphs and graph database technology are necessary to better understand data.

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

Other graph algorithms are applied to use cases including recommendations, fraud detection, network analysis, and natural language processing, constituting the domain of graph analytics.

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 ...

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 ...

Graph databases use graph structures for semantic queries, providing a more flexible and efficient way to manage relationships between data.

Perfect graphs are, by definition, colorable with the most limited palette possible. When coloring a graph, every node in a mutually connected cluster, or “clique,” must receive a distinct ...