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Abstract:
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With network data becoming ubiquitous in many applications, many models and algorithms for network analysis have been proposed, yet methods for providing uncertainty estimates are much less common. Bootstrap and other resampling procedures have been an effective general tool for estimating uncertainty from i.i.d. samples, but resampling network data is substantially more complicated, since we typically only observe one network. This talk will provide an overview of several recent resampling methods we have developed for networks, and discuss parametric vs nonparametric bootstrap in this setting.
Based on joint work with Keith Levin and Qianhua Shan.
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