How Big Data Carried Graph Theory Into New Dimensions

The mathematical language for talking about connections, which usually depends on networks--vertices (dots) and edges (lines connecting them)--has been an invaluable way to model real-world phenomena since at least the 18th century. But a few decades ago, the emergence of giant data sets forced researchers to expand their toolboxes and, at the same time, gave them sprawling sandboxes in which to apply new mathematical insights. Since then, said Josh Grochow, a computer scientist at the University of Colorado, Boulder, there's been an exciting period of rapid growth as researchers have developed new kinds of network models that can find complex structures and signals in the noise of big data. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. Grochow is among a growing chorus of researchers who point out that when it comes to finding connections in big data, graph theory has its limits.