Uncertainty Propagation from Network Inference to Network Characterization

• Activity Number: 124
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Weston Viles*+ and Eric Kolaczyk
• Companies: Boston University and Boston University

Abstract

Network-based data (e.g., from sensor, social, biological, and information networks) now play an important role across the sciences. Frequently the graphs used to represent networks are inferred from data. Surprisingly, however, in characterizing the higher-level properties of these networks (e.g., density, clustering, centrality), the uncertainty in their inferred topology typically is ignored. The distribution of estimators characterizing these networks defined implicitly through standard thresholding procedures can have distributions complicated by dependence inherent among the thresholded events. Motivated by this observation, we present a method by which the distribution of a sum of dependent binary random variables is approximated and demonstrate the method by exploring the problem of estimating network density - a simple but fundamental characterization of a network - in the context of correlation networks with Gaussian noise.