On the Spread of Graph-Related Matrices

Abstract: Let $M$ be an $n\times n$ real symmetric matrix. The spread of $M$ is defined as the difference between its largest and smallest eigenvalue. When considering the spread of graph-related matrices, this topic has attracted significant attention, resulting in a substantial collection of findings. In this paper, we study a general spread problem regarding $A_{\alpha}$-matrix of graphs. The $A_{\alpha}$-matrix of a graph $G$, introduced by Nikiforov in 2017, is a convex combinations of its diagonal degree matrix $D(G)$ and adjacency matrix $A(G)$, defined as $A_{\alpha} (G) = \alpha D(G) + (1-\alpha) A(G)$. Let $\lambda_1^{(\alpha)} (G)$ and $\lambda_n^{(\alpha)} (G)$ denote the largest and least eigenvalues of $A_{\alpha} (G)$, respectively. We determined the unique graph that maximizes $\lambda^{(\alpha)}_1 (G) - \beta\cdot\lambda^{(\gamma)}_n (G)$ among all connected $n$-vertex graphs for sufficiently large $n$, where $0 \leq \alpha < 1$, $1/2\leq \gamma < 1$ and $0<\beta\gamma\leq 1$. As applications, we confirm a conjecture proposed by Lin, Miao, and Guo [Linear Algebra Appl. 606 (2020) 1--22], and derive a new finding.