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Resampling methods for networks

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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 – that is, drawing observations repeatedly at random from an already observed sample – are an effective tool for estimating uncertainty in classical statistical settings, but resampling network data is substantially more complicated. This talk will provide a brief introduction to networks and modeling network data as random graphs, and then introduce several recent uses of resampling for network data.

A wine reception in the central core, will follow the talk

This talk is part of the Peter Whittle Lecture series.

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