AbstractSpectra and representations of some special weighted graphs are investigated with weight matrices consisting of homogeneous blocks. It is proved that a random perturbation of the weight matrix or that of the weighted Laplacian with a “Wigner-noise” will not have an effect on the order of the protruding eigenvalues and the representatives of the vertices will unveil the underlying block-structure.Such random graphs adequately describe some biological and social networks, the vertices of which belong either to loosely connected strata or to clusters with homogeneous edge-densities between any two of them, like the structure guaranteed by the Regularity Lemma of Szemerédi
We study random graphs with possibly different edge probabilities in the challenging sparse regime o...
International audienceIn this work, we study the spectrum of the normalized Laplacian and its regula...
Relevant information from networked systems can be obtained by analyzing the spectra of matrices ass...
AbstractSpectra and representations of some special weighted graphs are investigated with weight mat...
AbstractBehaviour of the eigenvalues of random matrices with an underlying linear structure is inves...
Eigenvectors of large matrices (and graphs) play an essential role in combinatorics and theoretical ...
AbstractThe asymptotic behaviour of the eigenvalues of random block-matrices is investigated with bl...
In this dissertation we study a problem in mathematical physics concerning the eigenvalues of random...
This paper studies how close random graphs are typically to their expectations. We interpret this qu...
AbstractWe prove that block random matrices consisting of Wigner-type blocks have as many large (str...
We analyze the eigenvalues of a random graph ensemble, proposed by Chung and Lu, in which a given se...
Within a random-matrix theory approach, we use the nearest-neighbour energy-level spacing distributi...
A random graph model is a set of graphs together with a probability distribution on that set. A rand...
Spectral graph theory widely increases the interests in not only discovering new properties of well ...
For each $N, let G N$ be a simple random graph on the set of vertices $[N ] = {1, 2,. .. , N }$, whi...
We study random graphs with possibly different edge probabilities in the challenging sparse regime o...
International audienceIn this work, we study the spectrum of the normalized Laplacian and its regula...
Relevant information from networked systems can be obtained by analyzing the spectra of matrices ass...
AbstractSpectra and representations of some special weighted graphs are investigated with weight mat...
AbstractBehaviour of the eigenvalues of random matrices with an underlying linear structure is inves...
Eigenvectors of large matrices (and graphs) play an essential role in combinatorics and theoretical ...
AbstractThe asymptotic behaviour of the eigenvalues of random block-matrices is investigated with bl...
In this dissertation we study a problem in mathematical physics concerning the eigenvalues of random...
This paper studies how close random graphs are typically to their expectations. We interpret this qu...
AbstractWe prove that block random matrices consisting of Wigner-type blocks have as many large (str...
We analyze the eigenvalues of a random graph ensemble, proposed by Chung and Lu, in which a given se...
Within a random-matrix theory approach, we use the nearest-neighbour energy-level spacing distributi...
A random graph model is a set of graphs together with a probability distribution on that set. A rand...
Spectral graph theory widely increases the interests in not only discovering new properties of well ...
For each $N, let G N$ be a simple random graph on the set of vertices $[N ] = {1, 2,. .. , N }$, whi...
We study random graphs with possibly different edge probabilities in the challenging sparse regime o...
International audienceIn this work, we study the spectrum of the normalized Laplacian and its regula...
Relevant information from networked systems can be obtained by analyzing the spectra of matrices ass...