We have amazing customers doing even more amazing things with their data. As we hear from our customers with their stories, we will be sharing them with you here on our blog. Hopefully they will help inspire you to think about what more you could be doing with your own data! Contact us if you have a case study using Eureqa you would like to share.
Network theory has found strong applications in fields from social sciences to physics. One prominent way that network theory can be applied is in social networks, to examine the structure of relationships between different social entities on the network. Using network theory, researchers can discover nodes (people) that have been hidden from the networks, and even pinpoint fictional nodes that have been implanted into the network.
Josh Bongard, professor of computer science at the University of Vermont, was already familiar with the Eureqa software from communications with Michael Schmidt. He and fellow professor, Jim Bagrow, decided to pit Eureqa against their enormous dataset of simulated social networks to create models that could discover missing nodes.
Bongard explains, “We could have used a support vector machine or some other state-of-the-art linear or nonlinear regression method to get a low order polynomial approximation, but that wouldn’t have given us any real insight into the nature of the relationship between information flow and network structure.”
For more details, read the full case study here!