Spectral Graph Theory and the Inverse Eigenvalue Problem of a Graph

Dear Colleagues,

Spectral Graph Theory is the study of the spectra of certain matrices defined from a given graph, including the adjacency matrix, the Laplacian matrix, and other related matrices. Graph spectra have been studied extensively for more than fifty years. The Inverse Eigenvalue Problem of a Graph seeks to determine information about the possible spectra of the real symmetric matrices whose pattern of nonzero entries is described by a given graph.

The special issue of "Spectral Graph Theory and the Inverse Eigenvalue Problem of a Graph" is devoted to a variety of topics in spectral graph theory and the inverse eigenvalue problem of a graph, including but not limited to studying the eigenvalues and eigenvectors of certain graph matrices, minimum rank, maximum nullity and the minimum number of distinct eigenvalues of a graph and their applications. The main aim of this Special Issue is to encourage new theoretical results in spectral graph theory, and the inverse eigenvalue problem of a graph as well as the interaction of spectral graph theory and the inverse eigenvalue problem of a graph with other mathematical sciences.

Prof. Dr. Shaun Fallat
Dr. Seyed Ahmad Mojallal
Dr. Mahsa N. Shirazi
Guest Editors

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• spectral graph theory
• graph matrices
• adjacency matrix
• Laplacian matrix
• graph eigenvalues
• graph eigenvectors
• inverse eigenvalue problem of a graph
• maximum nullity
• minimum rank