Abstract #302303

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JSM 2003 Abstract #302303
Activity Number: 102
Type: Invited
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #302303
Title: Linking Random Graph and Loglinear Models of Networks
Author(s): Martina Morris*+ and Steven Goodreau
Companies: University of Washington and University of Washington
Address: Dept. of Sociology, Seattle, WA, 98195-0001,
Keywords: networks ; graph models ; epidemiology ; HIV ; AIDS
Abstract:

Much progress has been made on the development of statistical methods for network analysis. Building on the general class of exponential random graphs, a range of new statistical models have been proposed, including Markov random graphs, "p-star" models, and actor-oriented models, to capture the systematic patterns of association and dyadic dependence in networks. This class of models is related to the log-linear models used in earlier work to analyze mixing patterns in local network data. Both approaches are based on the exponential family. Random graphs model the probability that two actors form a partnership given their attributes and the rest of the data, while log-linear approaches model the probability that two actors have specific attributes given that they form a partnership. Under dyadic independence the two probabilities are related via Bayes rule, and the parameters may in some cases be explicitly related. Understanding the relationship between the two models sheds light on the relationship between local and complete network data, and the role that models can play in bridging the traditional gap between them.

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