Higher-order Fluctuations in Dense Random Graph Models

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=eyaUaGpj4OM


Duration: 40:30
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Gursharn Kaur (University of Virgina)
https://simons.berkeley.edu/node/22604
Graph Limits, Nonparametric Models, and Estimation

In this talk, I will present the multivariate normal approximation for the centered subgraph counts in dense random graphs. The main motivation to investigate these statistics is the fact that they are key to understanding fluctuations of regular subgraph counts since they act as an orthogonal basis of a corresponding L2 space. We also identify the resulting limiting Gaussian stochastic measures by means of the theory of generalised U-statistics and Gaussian Hilbert spaces, which we think is a suitable framework to describe and understand higher-order fluctuations in dense random graph models.
This is joint work with Adrian Roellin.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Graph Limits Nonparametric Models and Estimation
Gursharn Kaur