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Sparse graphs using exchangeable random measures

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If you have a question about this talk, please contact Quentin Berthet.

In this talk I will present a novel class of random graphs using exchangeable random measures on the plane. The construction builds on the framework of completely random measures (CRM). For certain classes of CRMs, the associated graphs are sparse with heavy-tailed degree distributions, with a single parameter tuning the sparsity of the graph. Posterior inference of the parameters of the graph can be carried out through a Markov chain Monte Carlo algorithm that alternates between Hamiltonian and Metropolis-Hastings updates. I then explore network properties in a range of real datasets, including Facebook social circles, a political blogosphere, protein networks, citation networks, and world wide web networks, including networks with hundreds of thousands of nodes and millions of edges.

This talk is part of the Statistics series.

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