Partnership Duration Analysis with Dynamic Social Network Using Separable Temporal Exponential Random Graph Model

• Activity Number: 696
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Ke Li*+ and Martina Morris
• Companies: University of Washington and University of Washington
• Keywords: ERGM ; Survival Analysis ; Latent Mixture Model ; Social Network ; Duration Analysis

Abstract

Our goal is to estimate partnership durations in a social network. The partnership data come from the "Know Your Network" (KYN) project, which uses an ego-centric sampling design that contains both right censoring and left truncation cases. We explore four different models for partnership duration, with a range of different estimation techniques based on methods from survival analysis. The models incorporate different forms of node and tie heterogeneity. We analyze goodness-of-fit using newly developed dynamic network simulation methods (separable temporal ERGMs), simulating the network of partnerships over time, and replicating the sampling design from the KYN project. We find the best fitting model is a latent mixture exponential with two types of partnerships.