NETS 7052 — Computational Methods for Network Science
4 semester hoursGraduateLecture
Introduces computational methods for analyzing and modeling complex networks across scientific domains. Emphasizes programming-based workflows for working with network data including data collection, cleaning, representation, and visualization. Covers foundational concepts in network analysis; random graph models; community detection; machine learning for network data; network dynamics such as diffusion, contagion, and random walks; network sampling and sparsification; temporal and spatial networks; and network comparison and reconstruction. Offers students a structured opportunity to implement algorithms with feedback, interpret results, and communicate network-based insights in technical and interdisciplinary contexts.