Grace Brooks ’21; Marston Xue ’22; Shu Zhou ’21
Majors: Mathematics; Computer Science
Research Collaborator: Maggie Mehlmann
Faculty Collaborator: Andrea Bruder, Mathematics & Computer Science
In this research we expanded upon a previously researched mathematical model describing the tendency of similar people to associate and form social networks. We used a stochastic or somewhat randomized model to transform data describing the similarities between people into an adjacency matrix that describes whether people in the network are connected. The adjacency matrix can then be visualized with a network of nodes and edges. We used GPA data describing students at a Russian high school and were able to compare our modeled social network to how the students were actually connected based on data collected from their social medias. We used basic graph theory to gauge how similar our modeled social network was to the actual network and adjusted the model to better fit the data.