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Nonparametrics for networks

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SNAW01 - Graph Limits and Statistics

Relational data have become an important component of modern statistics. Networks, and weighted networks, are ubiquitous in modern applications such as disease dynamics, ecology, financial contagion, and neuroscience. The inference of networks is harder, in parts because the measure placed on the observables need to satisfy sets of permutation invariances, and most networks are very sparse, with most possible relations not present.

This talk will explore how to best construct nonparametric summaries of such objects, in such as way that the underlying statistical model of the observations is well described, and any estimators computable with scalable algorithms.

This is joint work with Pierre-Andre Maugis and Patrick Wolfe (UCL).

This talk is part of the Isaac Newton Institute Seminar Series series.

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