Understanding the behaviour of large networks

OMI Seminar Series

In this talk – which will be accessible to a general audience – we show how the asymptotic behavior of random networks gives rise to universal statistical summaries. These summaries are related to concepts that are well understood in the other contexts outside of Big Data – such as stationarity and ergodicity – but whose extension to networks requires recent developments from the theory of graph limits and the corresponding analog of de Finetti’s theorem. We introduce a new tool based on these summaries, which we call a network histogram, obtained by fitting a statistical model called a blockmodel to a large network. Blocks of edges play the role of histogram bins, and so-called network community sizes that of histogram bandwidths or bin sizes. For more details, see recent work in the Proceedings of the National Academy of Sciences (doi:10.1073/pnas.1400374111, with Sofia Olhede) and the Annals of Statistics (doi:10.1214/13-AOS1173, with David Choi).

Tuesday, February 9, 2016 - 12:30
to 13:30