
handle: 10945/3852
In Graph Theory, every graph can be expressed in terms of certain real, symmetric matrices derived from the graph, most notably the adjacency or Laplacian matrices. Spectral Graph Theory focuses on the set of eigenvalues and eigenvectors, called the spectrum, of these matrices and provides several interesting areas of study. One of these is the inverse eigenvalue problem of a graph, which tries to determine information about the possible eigenvalues of the real symmetric matrices whose pattern of nonzero entries is described by a given graph. A second area is the energy of a graph, defined to be the sum of the absolute values of the eigenvalues of the adjacency matrix of that graph. Here we explore these two areas for the hypercube Qn, which is formed recursively by taking the Cartesian product of Qn-1 with the complete graph on two vertices, K2. We analyze and compare several key ideas from the inverse eigenvalue problem for Qn, including the maximum multiplicity of possible eigenvalues, the minimum rank of possible matrices, and the number of paths that occur both as induced subgraphs and after deleting certain vertices. We conclude by deriving several equations for the energy of Qn.
http://archive.org/details/spectralgraphory109453852
US Army (USA) author.
Graph theory, Matrices
Graph theory, Matrices
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
