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Lab Well Represented at Sunbelt XXX

Published on June 29, 2010 by

Several lab members presented research at this year’s annual Sunbelt Conference.

 Lab PI Carter Butts presented a new method for exact sampling from general network models in exponential family form. This technique offers the first practical procedure for simulating the behavior of complex network models without resorting to approximations such as the widely used Markov chain Monte Carlo approach. The method has the potential to improve our ability to estimate the effects of competing mechanisms in social network models, and to aid in the study of how dependence among relationships shapes network structure.

Zack Almquist presented “Predicting Regional Self-Identification from Spatial Network Models” which employs large-scale spatial network simulations in order to predict regional identification in the US.

Sean Fitzhugh’s work on network robustness in the context of the World Trade Center disaster response was presented by Butts. He finds that organizations with day-to-day involvement with disaster response are less robust to loss of personnel with coordinative roles than are organizations without such involvement with disaster response.

Lorien Jasny concluded her stay in Italy as a visiting fellow at Trento University as she worked on networks of immigrant advocacy groups with Professor Mario Diani. Her Sunbelt presentation looked at structure in propositional networks of political behavior and formulated appropriate baseline models.

Emma Spiro presented research on two projects. Her work extends traditional measures of brokerage by exploring brokerage behaviour and opportunity in dynamic networks. Her work also includes a new measure of dynamic brokerage as she she demonstrated in a case study of interorganizational collaboration. She also used data from a micro-blogging service to examine changes in users’ local networks following exogenous, hazard-related events. She utilized the longitudinal nature of the data to examine seasonality as well as endogenous and exogenous variation in the pace of change of local network dynamics.

 
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