36,025 research outputs found
Moment-Based Spectral Analysis of Random Graphs with Given Expected Degrees
In this paper, we analyze the limiting spectral distribution of the adjacency
matrix of a random graph ensemble, proposed by Chung and Lu, in which a given
expected degree sequence
is prescribed on the ensemble. Let if there is an edge
between the nodes and zero otherwise, and consider the normalized
random adjacency matrix of the graph ensemble: . The empirical spectral distribution
of denoted by is the empirical
measure putting a mass at each of the real eigenvalues of the
symmetric matrix . Under some technical conditions on the
expected degree sequence, we show that with probability one,
converges weakly to a deterministic
distribution . Furthermore, we fully characterize this
distribution by providing explicit expressions for the moments of
. We apply our results to well-known degree distributions,
such as power-law and exponential. The asymptotic expressions of the spectral
moments in each case provide significant insights about the bulk behavior of
the eigenvalue spectrum
Resolvent of Large Random Graphs
We analyze the convergence of the spectrum of large random graphs to the
spectrum of a limit infinite graph. We apply these results to graphs converging
locally to trees and derive a new formula for the Stieljes transform of the
spectral measure of such graphs. We illustrate our results on the uniform
regular graphs, Erdos-Renyi graphs and preferential attachment graphs. We
sketch examples of application for weighted graphs, bipartite graphs and the
uniform spanning tree of n vertices.Comment: 21 pages, 1 figur
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