书目名称 | Locating Eigenvalues in Graphs |
副标题 | Algorithms and Appli |
编辑 | Carlos Hoppen,David P. Jacobs,Vilmar Trevisan |
视频video | |
概述 | Offers a careful exposition of a class of important eigenvalue location algorithms for various graph classes.Introduces spectral graph theory and graph representations concisely.Describes applications |
丛书名称 | SpringerBriefs in Mathematics |
图书封面 |  |
描述 | This book focuses on linear time eigenvalue location algorithms for graphs. This subject relates to spectral graph theory, a field that combines tools and concepts of linear algebra and combinatorics, with applications ranging from image processing and data analysis to molecular descriptors and random walks. It has attracted a lot of attention and has since emerged as an area on its own..Studies in spectral graph theory seek to determine properties of a graph through matrices associated with it. It turns out that eigenvalues and eigenvectors have surprisingly many connections with the structure of a graph. This book approaches this subject under the perspective of eigenvalue location algorithms. These are algorithms that, given a symmetric graph matrix M and a real interval I, return the number of eigenvalues of M that lie in I. Since the algorithms described here are typically very fast, they allow one to quickly approximate the value of any eigenvalue, which is a basic step in most applications of spectral graph theory. Moreover, these algorithms are convenient theoretical tools for proving bounds on eigenvalues and their multiplicities, which was quite useful to solve longstandi |
出版日期 | Book 2022 |
关键词 | spectrum; linear-time algorithm; graph; adjacency matrix; Laplacian matrix; tree; spectral graph theory; ei |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-031-11698-8 |
isbn_softcover | 978-3-031-11697-1 |
isbn_ebook | 978-3-031-11698-8Series ISSN 2191-8198 Series E-ISSN 2191-8201 |
issn_series | 2191-8198 |
copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |