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A new method to reduce overestimation of thresholds with observational network data

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  • Berry, George
  • Cameron, Christopher John

Abstract

Networks of interdependent nodes support phenomena such as epidemics, product adoption, cascading failure, ecosystem collapse, congestion, and bandwagon effects. We consider the problem of using observational data to estimate the sensitivity of individual nodes to the activation of their network neighbors. We prove that—in the case of binary activation decisions—activation thresholds are impossible to correctly measure for some nodes in virtually all contagion processes on complex networks. This result holds even when each step of the process is observed. Measurement error always produces an overestimate of a node's true activation threshold. We develop a condition for determining which node thresholds are correctly measured and demonstrate that modeling activation thresholds as a function of node-level factors reduces the error compared to existing approaches.

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  • Berry, George & Cameron, Christopher John, 2017. "A new method to reduce overestimation of thresholds with observational network data," SocArXiv ctjd6_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:ctjd6_v1
    DOI: 10.31219/osf.io/ctjd6_v1
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