Our opinions and judgments are increasingly shaped by what we read on social media. Understanding how users in a network update opinions is important in the context of viral marketing, information dissemination, and targeting messages to users in the network. In this project I would like to apply network science concepts to show the students how our perception of the world is shaped by the way we are connected in a network. We model people as the nodes of a graph and see how the topology of this graph affects the opinion dynamics. We also will see connectivity and interactions among individuals can speed up or slow down propagation of some news and come up with effective strategies to prevent the propagation or to facilitate it.
We introduced the interns to the fundamentals of network science, a fast-growing and vibrant field in science and technology. We discussed: graph concepts and metrics, basic network models, and their properties. To see application of this field we focused on consensus in opinion dynamics and epidemic propagation in a population modeled by graphs. Example codes were provided in MATLAB and Python to better grasp these concepts. The interns learned about graph theory, differential equations, and how to simulate them in MATLAB. At the end they learned how to effectively present what they have learned and have done to the audience.