Statistical Approaches to Dynamic Networks in Society
MCML Authors
Cornelius Fritz
Dr.
Abstract
Cornelius Fritz
Dr.
Abstract
This dissertation focuses on dynamic networks in the Social Sciences, examining methods and applications in network modeling. Part two provides an overview of modeling frameworks for dynamic networks, including applications in studying COVID-19 infections using social connectivity as covariates. In part three, the dissertation introduces a Signed Exponential Random Graph Model (SERGM) for signed networks and a bipartite variant of the Temporal Exponential Random Graph Model (TERGM) to study co-inventorship in patents. Part four concludes with models for event networks, including a Relational Event Model for Spurious Events (REMSE) to manage false-discovery rates in event data. (Shortened).
BibTeXKey: Fri22