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EIGENVALUE AND EIGENVECTOR INFORMATION OF GRAPHS AND THEIR VALIDITY IN DETECTION OF GRAPH ISOMORPHISM

机译:图中的特征值和特征向量信息及其在图中的检测中的效力

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Detection of graph isomorphism (GI) has been widely used in many fields in science and engineering. Currently, a potential application of GI detection could be in molecular structure design for microelectromechanical systems and nano-systems. In this paper, we discuss the relationship between graphs and their eigenvalues as well as unique eigenvectors. We prove that the graphs having all distinct eigenvalues are isomorphic if and only if they have the same graph spectrum and the equivalent eigenvectors. The graphs having coincident eigenvalues might be isomorphic if they have the same graph spectrum and the equivalent unique eigenvectors. Further, a convergent recursive procedure is given to subdivide a group-to-group mapping once appeared in the graphs having coincident eigenvalues to seek potential one-to-one mappings so as to determining if the graphs are isomorphic.
机译:图同构同位(GI)的检测已被广泛应用于科学和工程的许多领域。目前,GI检测的潜在应用可以是微机电系统和纳米系统的分子结构设计。在本文中,我们讨论了图形与他们的特征值之间的关系以及独特的特征向量。我们证明,如果它们具有相同的图谱和等同的特征向量,则具有所有不同特征值的图表是同性的。如果它们具有相同的图谱和等同的独特特征向量,则具有重合特征值的图表可能是同性的。此外,给予收敛递归过程来细分组到组映射一次,该映射一次出现在具有重合特征值的图中,以寻找潜在的一对一映射,以确定图形是否是同性的。

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