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=iIfGqFUBnXk


Duration: 45:06
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Adrian Roellin (National University of Singapore)
https://simons.berkeley.edu/node/22596
Graph Limits, Nonparametric Models, and Estimation

Dense graph limit theory is mainly concerned with law-of large-number type of results. We propose a corresponding central limit theorem - or rather fluctuation theory - based on Janson's theory of Gaussian Hilbert Spaces and generalised U-statistics from the 1990s. Our approach provides rates and allows for proper statistical inference based on subgraph counts.



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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
Adrian Roellin