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Lab Members Present at XXXII Sunbelt Conference

Published on March 18, 2012 by

Lab members Carter Butts, Zack Almquist, Emma Spiro, Sean Fitzhugh, Adam Boessen, Ragupathyraj Valluvan, and Nicole Beckage presented research at the INSNA Sunbelt XXXII Conference in Redondo Beach, California. Lab alumni Ryan Acton, Chris Marcum, Lorien Jasny and Miruna Petrescu-Prahova also presented at the conference.

Zack Almquist presented on Sunday, March 18th work, coauthored with Carter Butts, titled “Network Diffusion and the
Effects of Geographic Heterogeneity within Human Populations.”

Emma Spiro presented work from the HEROIC Project entitled “A Microstructure Topology for Hyperedge Communication Events.”  She also co-presented with collaborator Britta Johnson from UCCS work titled “Disruptive Diffusion: Adoption of Microblog Technologies Among Urban Organizations.”

Sean Fitzhugh presented ‘Link Trace Methods for Enumeration Vertex Sets,’ which focuses on methods for efficiently locating all members of a subpopulation within a network. He demonstrated methods which are more efficient than traditional link trace methods (e.g. random walk or BFS) and which also simultaneously allow us to estimate the size of the entire network and the size of our subpopulation of interest.

Adam Boessen presented “If there’s a crime in your neighborhood, who you gonna call?” a project co-authored with John Hipp, Carter Butts, Nicholas Nagle, Ryan Acton, Christopher Marcum, and Zack Almquist. Using the Twin Communities Network Study from the American Social Fabric Project, they examined how the spatial distribution of ties affect whom people seek when concerned about the safety of their neighborhood.  Rather than relying on the immediate area surrounding their home, the results suggest that residents more frequently reach out to alters that are flung far beyond the local area.
Ragupathyraj Valluvan presented on Friday, March 16th work coauthored with Zack Almquist, Carter Butts and Animashree Anandkumar,
titled “Semi-Parametric vertex set prediction for dynamic networks using latent tree models.”
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