An hour into Bassett's Tuesday class, the students whip out their laptops and become subjects in one of her latest studies about learning. Their screens display a cloud of about 50 concepts she has selected from the course, such as prediction, network, behavior, and neurological disease. They draw lines to connect related words and phrases, stretching the lines to put distance between dissimilar concepts. Bassett will compare the structure of the maps at different points in the course, gauge the influence of class readings and lectures, and look for correlations between network structure and test scores. The work seems miles away from Bassett's physics degree. But underlying that study—and nearly every other project in her lab—is a branch of math called graph theory. The approach, with roots in the 18th century, describes the structure of networks of discrete, interacting parts, be they friends linked on social media or grains in a sand pile. Researchers first calculate the relationships between all nodes in a network: in the simplest case, either a zero (not connected) or a one (connected). Then, they ask questions about the features of the network: Is it a sparse web or a dense jungle of connections? Do certain nodes have an unusually large number of connections? Do nodes tend to organize themselves into tight-knit modules that mostly talk among themselves?
related exploration
The Curious Human a talk by Dani Bassett
article 
on ‘kinesthetic signature'
paper on curiosity
Dense Meaning: correlation with garden experiences