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Inference and large-scale structure in networks

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SNA - Theoretical foundations for statistical network analysis

Characterization of network structure as focused on two different scales: small-scale structure, represented by properties such as degrees, correlations, and clustering, and large-scale structure, which is most commonly presented in terms of modules and community detection.  This talk will focus on large-scale structure, but with the aim of getting away from community structure, which is well-trodden ground, and looking at other forms.  Working with generative models and a range of model-based inference techniques, I'll talk about overlapping communities, hierarchical structure, latent-space structure, ranking, and core-periphery structure, among others.

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

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