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Abstract Details

Activity Number: 465
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #304328
Title: Sampling for Conditional Inference on Network Data
Author(s): Jingfei Zhang*+ and Yuguo Chen
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Address: 609 W MAIN ST, URBANA, IL, 61801, United States
Keywords: Counting problem ; Exact test ; Random graphs ; Random networks ; Sequential importance sampling

Random graphs with given vertex degrees have been widely used as a model for many real-world complex networks. We describe two sequential sampling methods for sampling networks with a given degree sequence. These samples can be used to approximate closely the null distributions of a number of test statistics involved in such networks, and provide an accurate estimate of the total number of networks with given vertex degrees. We apply our method to a range of examples to demonstrate its efficiency in real problems.

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