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This content will become publicly available on June 1, 2026

Title: The Large Deviation Principle for W-random spectral measures
The 𝑊 -random graphs provide a flexible framework for modeling large random networks. Using the Large Deviation Principle (LDP) for 𝑊 -random graphs from [19], we prove the LDP for the corresponding class of random symmetric Hilbert-Schmidt integral operators. Our main result describes how the eigenvalues and the eigenspaces of the integral operator are affected by large deviations in the underlying random graphon. To prove the LDP, we demonstrate continuous dependence of the spectral measures associated with integral operators on the corresponding graphons and use the Contraction Principle. To illustrate our results, we obtain leading order asymptotics of the eigenvalues of small-world and bipartite random graphs conditioned on atypical edge counts. These examples suggest several representative scenarios of how the eigenvalues and the eigenspaces are affected by large deviations. We discuss the implications of these observations for bifurcation analysis of Dynamical Systems and Graph Signal Processing.  more » « less
Award ID(s):
2408008 2406941
PAR ID:
10610590
Author(s) / Creator(s):
;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Applied and Computational Harmonic Analysis
Volume:
77
Issue:
C
ISSN:
1063-5203
Page Range / eLocation ID:
101756
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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