Functional limit theorems for random regular graphs

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Added some references and corrected typos. Added Remark 38. 39 pages

Scientific paper

Consider d uniformly random permutation matrices on n labels. Consider the sum of these matrices along with their transposes. The total can be interpreted as the adjacency matrix of a random regular graph of degree 2d on n vertices. We consider limit theorems for various combinatorial and analytical properties of this graph (or the matrix) as n grows to infinity, either when d is kept fixed or grows slowly with n. In a suitable weak convergence framework, we prove that the (finite but growing in length) sequences of the number of short cycles and of cyclically non-backtracking walks converge to distributional limits. We estimate the total variation distance from the limit using Stein's method. As an application of these results we derive limits of linear functionals of the eigenvalues of the adjacency matrix. A key step in this latter derivation is an extension of the Kahn-Szemer\'edi argument for estimating the second largest eigenvalue for all values of d and n.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Functional limit theorems for random regular graphs does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Functional limit theorems for random regular graphs, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Functional limit theorems for random regular graphs will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-97337

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.