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Activity Number: 696
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #310011
Title: Dynamic Social Network Analysis Using Separable Temporal Exponential Random Graph Models
Author(s): Gregory J. Lambert*+ and Patrick D. Finley and Thomas W. Moore
Companies: Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories
Keywords: Exponential Random Graph Models ; Social Network Analysis ; Separable Temporal Exponential Random Graph Models
Abstract:

The structure of social networks is derived from the presence (or absence) of relational ties. Tie formations in social networks are often conditional on pre-existing ties within the network. In combination with the purely structural characteristics in social networks, this conditional dependency creates a unique set of problems from a modeling perspective. Exponential random graph models (ERGMs) form a flexible modeling paradigm that can be used to conduct statistical inference on social networks to inform analysis. We extend the cross-sectional ERGM framework to model a dynamic network in discrete time using a newly developed statistical method referred to as separable temporal exponential random graph models (STERGMs).

Fitted STERGMs are compared to a subset of the National Longitudinal Study of Adolescent Health data to validate the model representation of evolving friendship networks, with directional friendship nominations characterized as tie formations. Inferential network analysis is conducted using the STERGMs to examine the effects of adolescent smoking on peer friendship nominations.


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