A graph operation and its applications in generating orderenergetic and equienergetic graphs

A general graph operation is defined and some of its applications are given in this paper. The adjacency spectrum of any graph generated by this operation is given. A method for generating integral graphs using this operation is discussed. Corresponding to any given graph, we can generate an infinite sequence of pair of equienergetic non-cospectral graphs using this graph operation. Given an orderenergetic graph, it is shown that we can construct two different sequences of orderenergetic graphs. A condition for generating orderenergetic graphs from non-orderenergetic graphs are also derived. This method of constructing connected orderenergetic graphs solves one of the open problem stated in the paper by Akbari et al.(2020).

Cospectral constructions for several graph matrices using cousin vertices

Graphs can be associated with a matrix according to some rule and we can find the spectrum of a graph with respect to that matrix. Two graphs are cospectral if they have the same spectrum. Constructions of cospectral graphs help us establish patterns about structural information not preserved by the spectrum. We generalize a construction for cospectral graphs previously given for the distance Laplacian matrix to a larger family of graphs. In addition, we show that with appropriate assumptions this generalized construction extends to the adjacency matrix, combinatorial Laplacian matrix, signless Laplacian matrix, normalized Laplacian matrix, and distance matrix. We conclude by enumerating the prevelance of this construction in small graphs for the adjacency matrix, combinatorial Laplacian matrix, and distance Laplacian matrix.

On the spectra of a new duplication based corona of graphs

In this paper we introduce a new corona-type product of graphs namely duplication corresponding corona. Here we mainly determine the adjacency, Laplacian and signless Laplacian spectra of the new graph product. In addition to that, we find out the incidence energy, the number of spanning trees, Kirchhoff index and Laplacian-energy-like invariant of the new graph. Also we discuss some new classes of cospectral graphs.

Cospectral graphs for the normalized Laplacian

<abstract><p>Let $ G(a_1, a_2, \ldots, a_k) $ be a simple graph with vertex set $ V(G) = V_1\cup V_2\cup \cdots \cup V_k $ and edge set $ E(G) = \{(u, v)|u\in V_i, v\in V_{i+1}, i = 1, 2, \ldots, k-1\} $, where $ |V_i| = a_i &gt; 0 $ for $ 1\leq i\leq k $ and $ V_i\cap V_j = \emptyset $ for $ i\neq j $. Given two positive integers $ k $ and $ n $, and $ k-2 $ positive rational numbers $ t_2, t_3, \ldots, t_{\lceil k/2\rceil} $ and $ t_2', t_3', \ldots, t_{\lfloor k/2\rfloor}' $, let $ \Upsilon(n; k)_t^{t'} = \{G(a_1, a_2, \ldots, a_k)|\sum_{i = 1}^ka_i = n, a_{2i-1} = t_{i}a_1, a_{2j} = t_j'a_2, i = 2, 3, \ldots, \lceil k/2\rceil, $ $ j = 2, 3, \ldots, \lfloor k/2\rfloor; t = (t_2, t_3, \ldots, t_{\lceil k/2\rceil}), t' = (t_2', t_3', \ldots, t_{\lfloor k/2\rfloor}'); a_s\in N, 1\leq s\leq k\} $, where $ N $ is the set of positive integers. In this paper, we prove that all graphs in $ \Upsilon(n; k)_t^{t'} $ are cospectral with respect to the normalized Laplacian if it is not an empty set.</p></abstract>

Graphs that are cospectral for the distance Laplacian

The distance matrix $\mathcal{D}(G)$ of a graph $G$ is the matrix containing the pairwise distances between vertices, and the distance Laplacian matrix is $\mathcal{D}^L (G)=T(G)-\mathcal{D} (G)$, where $T(G)$ is the diagonal matrix of row sums of $\mathcal{D}(G)$. Several general methods are established for producing $\mathcal{D}^L$-cospectral graphs that can be used to construct infinite families. Examples are provided to show that various properties are not preserved by $\mathcal{D}^L$-cospectrality, including examples of $\mathcal{D}^L$-cospectral strongly regular and circulant graphs. It is established that the absolute values of coefficients of the distance Laplacian characteristic polynomial are decreasing, i.e., $|\delta^L_{1}|\geq \cdots \geq |\delta^L_{n}|$, where $\delta^L_{k}$ is the coefficient of $x^k$.