The normalized distance Laplacian

IF 1 Q2 MATHEMATICS Special Matrices Pub Date : 2019-03-11 DOI:10.1515/spma-2020-0114
Carolyn Reinhart
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引用次数: 8

Abstract

Abstract The distance matrix 𝒟(G) of a connected graph G is the matrix containing the pairwise distances between vertices. The transmission of a vertex vi in G is the sum of the distances from vi to all other vertices and T(G) is the diagonal matrix of transmissions of the vertices of the graph. The normalized distance Laplacian, 𝒟𝒧(G) = I−T(G)−1/2 𝒟(G)T(G)−1/2, is introduced. This is analogous to the normalized Laplacian matrix, 𝒧(G) = I − D(G)−1/2A(G)D(G)−1/2, where D(G) is the diagonal matrix of degrees of the vertices of the graph and A(G) is the adjacency matrix. Bounds on the spectral radius of 𝒟 𝒧 and connections with the normalized Laplacian matrix are presented. Twin vertices are used to determine eigenvalues of the normalized distance Laplacian. The distance generalized characteristic polynomial is defined and its properties established. Finally, 𝒟𝒧-cospectrality and lack thereof are determined for all graphs on 10 and fewer vertices, providing evidence that the normalized distance Laplacian has fewer cospectral pairs than other matrices.
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归一化距离拉普拉斯函数
连通图G的距离矩阵(G)是包含顶点之间成对距离的矩阵。顶点vi在G中的传输是vi到所有其他顶点的距离的和,T(G)是图中顶点传输的对角矩阵。引入了归一化距离拉普拉斯函数,即:𝒧(G) = I−T(G)−1/2¾(G)T(G)−1/2。这类似于归一化拉普拉斯矩阵𝒧(G) = I−D(G)−1/2A(G)D(G)−1/2,其中D(G)是图中顶点度的对角矩阵,A(G)是邻接矩阵。给出了光谱半径的界和与归一化拉普拉斯矩阵的联系。利用双顶点确定归一化距离拉普拉斯函数的特征值。定义了距离广义特征多项式,并建立了其性质。最后,在10个或更少顶点的所有图中确定了𝒧-cospectrality和缺乏,提供了归一化距离拉普拉斯函数比其他矩阵具有更少的共谱对的证据。
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来源期刊
Special Matrices
Special Matrices MATHEMATICS-
CiteScore
1.10
自引率
20.00%
发文量
14
审稿时长
8 weeks
期刊介绍: Special Matrices publishes original articles of wide significance and originality in all areas of research involving structured matrices present in various branches of pure and applied mathematics and their noteworthy applications in physics, engineering, and other sciences. Special Matrices provides a hub for all researchers working across structured matrices to present their discoveries, and to be a forum for the discussion of the important issues in this vibrant area of matrix theory. Special Matrices brings together in one place major contributions to structured matrices and their applications. All the manuscripts are considered by originality, scientific importance and interest to a general mathematical audience. The journal also provides secure archiving by De Gruyter and the independent archiving service Portico.
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