Patterns and Surprises in Rich but Noisy Network Data

Explore the intricacies of network analysis in this seminar by Mark Newman, focusing on social and biological networks. Delve into the challenges of estimating network structure from rich but noisy data, including measurement errors, contradictory observations, and missing information. Examine how the pattern of errors in network data can provide valuable insights into both the data itself and the underlying systems. Learn about expectation maximization techniques, clustering coefficients, and their applications in various network types such as social networks, food webs, and plant-pollinator networks. Discover how to evaluate network analysis methods using ground truth, recall, and precision metrics. Gain a deeper understanding of network structure estimation and its implications for research in fields ranging from internet studies to biological systems.