We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation r...

Let G be a graph with n vertices and m edges and let μ1 (G) ≥ ... ≥ μn (G) be the eigenvalues of its adjacency matrix. We discuss the following general problem. For k fixed and n large, find or estimate fk (n) = max v(G)=n |μk (G)|+ ∣

The spectrum of a graph G is the set of eigenvalues of the 0–1 adjacency matrix of G. The nullity of a graph is the number of zeros in its spectrum. It is shown that the nullity of the line graph of a tree is at most one. c © 2001 Elsevier Science B.V. All rights reserved.

Relations between Laplacian eigenvectors and eigenvalues and the existence of almost equitable partitions (which are generalizations of equitable partitions) are presented. Furthermore, on the basis of some properties of the adjacency eigenvectors of a graph, a necessary and sufficient condition for the graph to be primitive strongly regular is introduced. c © 2006 Elsevier Ltd. All rights rese...

For a simple hypergraph H on n vertices, its Estrada index is defined as [Formula in text], where λ 1, λ 2,…, λ n are the eigenvalues of its adjacency matrix. In this paper, we determine the unique 3-uniform linear hypertree with the maximum Estrada index.

Let G be a simple graph with n vertices, and let A be the 0, 1 -adjacency matrix of G. We call det λI −A the characteristic polynomial of G, denoted by P G; λ , or abbreviated P G . Since A is symmetric, its eigenvalues λ1 G , λ2 G , . . . , λn G are real, and we assume that λ1 G ≥ λ2 G ≥ · · · ≥ λn G . We call λn G the least eigenvalue of G. Up to now, some good results on the least eigenvalue...

Let AG be the adjacency matrix of G. Let λ1 ≥ λ2 ≥ . . . ≥ λn be the eigenvalues of AG. Sometimes we will also be interested in the Laplacian matrix of G. This is defined to be LG = D−AG, where D is the diagonal matrix where Dvv equals the degree of the vertex v. For d-regular graphs, LG = dI −AG, and hence the eigenvalues of LG are d− λ1, d− λ2, . . . , d− λn. Lemma 1. • λ1 = d. • λ2 = λ3 = . ...

Abstract – Many real-world networks exhibit a high degeneracy at few eigenvalues. We show that a simple transformation of the network’s adjacency matrix provides an understanding to the origins of occurrence of high multiplicities in the networks spectra. We find that the eigenvectors associated with the degenerate eigenvalues shed light on the structures contributing to the degeneracy. Since t...

The energy of a graph G is equal to the sum of absolute values of the eigenvalues of the adjacency matrix of G, whereas the Laplacian energy of a graph G is equal to the sum of the absolute value of the difference between the eigenvalues of the Laplacian matrix of G and average degree of the vertices of G. Motivated by the work from Sharafdini et al. [R. Sharafdini, H. Panahbar, Vertex weighted...

Let $A(G)$ be the adjacency matrix and $D(G)$ diagonal of vertex degrees a simple connected graph $G$. Nikiforov defined $A_{\alpha}(G)$ convex combinations as $A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G)$, for $0\leq \alpha\leq 1$. If $ \rho_{1}\geq \rho_{2}\geq \dots \geq \rho_{n}$ are eigenvalues (which we call $\alpha$-adjacency $G$), \alpha $-adjacency energy $G$ is $E^{A_{\alpha}}(G)=\sum_{i...