Eigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus they may seem a surprising topic for a book. The authors propose two motivations for this new LNM volume: (1) There are fascinating subtle differences between the properties of solutions of Schrödinger equations on manifolds on the one hand, and their discrete analogs on graphs. (2) "Geometric" properties of (cost) functions defined on the vertex sets of graphs are of practical interest for heuristic optimization algorithms. The observation that the cost functions of quite a few of the well-studied combinatorial optimization problems are eigenvectors of associated graph Laplacians has prompted the investigation of such eigenvectors. The volu...
1. A study of graph eigenvectors shows connections to graph structure in ways that are reminiscent o...
Relations between Laplacian eigenvectors and eigenvalues and the existence of almost equitable parti...
We initiate a systematic study of eigenvectors of random graphs. Whereas much is known about eigenva...
Eigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus...
Eigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus...
These notes are not necessarily an accurate representation of what happened in class. The notes writ...
The graph Laplacian, a typical representation of a network, is an important matrix that can tell us ...
The graph Laplacian, a typical representation of a network, is an important matrix that can tell us ...
On the surface, matrix theory and graph theory are seemingly very different branches of mathematics....
summary:The perturbed Laplacian matrix of a graph $G$ is defined as $L^{\mkern -15muD}=D-A$, where $...
AbstractIf G is a graph, its Laplacian is the difference of the diagonal matrix of its vertex degree...
When attempting to develop wavelet transforms for graphs and networks, some researchers have used gr...
In the last decade important relations between Laplace eigenvalues and eigenvectors of graphs and se...
This thesis deals with optimizing extremal eigenvalues of weighted graph Laplacian matrices. In gene...
This thesis deals with optimizing extremal eigenvalues of weighted graph Laplacian matrices. In gene...
1. A study of graph eigenvectors shows connections to graph structure in ways that are reminiscent o...
Relations between Laplacian eigenvectors and eigenvalues and the existence of almost equitable parti...
We initiate a systematic study of eigenvectors of random graphs. Whereas much is known about eigenva...
Eigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus...
Eigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus...
These notes are not necessarily an accurate representation of what happened in class. The notes writ...
The graph Laplacian, a typical representation of a network, is an important matrix that can tell us ...
The graph Laplacian, a typical representation of a network, is an important matrix that can tell us ...
On the surface, matrix theory and graph theory are seemingly very different branches of mathematics....
summary:The perturbed Laplacian matrix of a graph $G$ is defined as $L^{\mkern -15muD}=D-A$, where $...
AbstractIf G is a graph, its Laplacian is the difference of the diagonal matrix of its vertex degree...
When attempting to develop wavelet transforms for graphs and networks, some researchers have used gr...
In the last decade important relations between Laplace eigenvalues and eigenvectors of graphs and se...
This thesis deals with optimizing extremal eigenvalues of weighted graph Laplacian matrices. In gene...
This thesis deals with optimizing extremal eigenvalues of weighted graph Laplacian matrices. In gene...
1. A study of graph eigenvectors shows connections to graph structure in ways that are reminiscent o...
Relations between Laplacian eigenvectors and eigenvalues and the existence of almost equitable parti...
We initiate a systematic study of eigenvectors of random graphs. Whereas much is known about eigenva...