Networks are sets of objects that are connected in some way and appear abundantly in nature, sociology, and technology. For many centuries, network theory focused on static networks, which are networks that do not change. However, since all networks transform over time, static networks have limited applications. By comparison, dynamic networks model how connections between objects change over time. In this work, we will explore how connections in dynamic networks change and how we can leverage these changes to make predictions about future iterations of networks. We will do this by first considering the link prediction problem, using either Katz distance or effective resistance to predict future connections, and relate these two metrics. Then we will look at using bipartite network connections to predict group transitions in professional sports teams. Lastly, we will investigate how to use network connections to identify and predict roles in social networks.
College and Department
Physical and Mathematical Sciences; Mathematics
BYU ScholarsArchive Citation
Jones, Rebecca Dorff, "Using Connections to Make Predictions on Dynamic Networks" (2022). Theses and Dissertations. 9388.
network theory, machine learning, Katz distance, link prediction, role identification, dynamic networks