25,698 research outputs found
Generalizations of the Strong Arnold Property and the minimum number of distinct eigenvalues of a graph
For a given graph G and an associated class of real symmetric matrices whose
off-diagonal entries are governed by the adjacencies in G, the collection of
all possible spectra for such matrices is considered. Building on the
pioneering work of Colin de Verdiere in connection with the Strong Arnold
Property, two extensions are devised that target a better understanding of all
possible spectra and their associated multiplicities. These new properties are
referred to as the Strong Spectral Property and the Strong Multiplicity
Property. Finally, these ideas are applied to the minimum number of distinct
eigenvalues associated with G, denoted by q(G). The graphs for which q(G) is at
least the number of vertices of G less one are characterized.Comment: 26 pages; corrected statement of Theorem 3.5 (a
Spectral Theory of Sparse Non-Hermitian Random Matrices
Sparse non-Hermitian random matrices arise in the study of disordered
physical systems with asymmetric local interactions, and have applications
ranging from neural networks to ecosystem dynamics. The spectral
characteristics of these matrices provide crucial information on system
stability and susceptibility, however, their study is greatly complicated by
the twin challenges of a lack of symmetry and a sparse interaction structure.
In this review we provide a concise and systematic introduction to the main
tools and results in this field. We show how the spectra of sparse
non-Hermitian matrices can be computed via an analogy with infinite dimensional
operators obeying certain recursion relations. With reference to three
illustrative examples --- adjacency matrices of regular oriented graphs,
adjacency matrices of oriented Erd\H{o}s-R\'{e}nyi graphs, and adjacency
matrices of weighted oriented Erd\H{o}s-R\'{e}nyi graphs --- we demonstrate the
use of these methods to obtain both analytic and numerical results for the
spectrum, the spectral distribution, the location of outlier eigenvalues, and
the statistical properties of eigenvectors.Comment: 60 pages, 10 figure
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