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Edge exchangeability: a new foundation for modeling network data

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SNAW05 - Bayesian methods for networks

Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. To address this issue, we introduce the principle of edge exchangeability, which is more natural for most applications and admits models for networks with sparse and/or power law structure. The vertices in an edge exchangeable network arrive in size-biased order according to their degree, further explaining why vertex exchangeability is an untenable assumption for many applications.

Our discussion settles a longstanding question of statistical network modeling and presents a new framework within which to develop future theory and methods.

Joint work with Walter Dempsey.

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

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