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Titlebook: Locating Eigenvalues in Graphs; Algorithms and Appli Carlos Hoppen,David P. Jacobs,Vilmar Trevisan Book 2022 The Editor(s) (if applicable)

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發(fā)表于 2025-3-21 16:57:56 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Locating Eigenvalues in Graphs
副標(biāo)題Algorithms and Appli
編輯Carlos Hoppen,David P. Jacobs,Vilmar Trevisan
視頻videohttp://file.papertrans.cn/588/587775/587775.mp4
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
叢書名稱SpringerBriefs in Mathematics
圖書封面Titlebook: Locating Eigenvalues in Graphs; Algorithms and Appli Carlos Hoppen,David P. Jacobs,Vilmar Trevisan Book 2022 The Editor(s) (if applicable)
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
關(guān)鍵詞spectrum; linear-time algorithm; graph; adjacency matrix; Laplacian matrix; tree; spectral graph theory; ei
版次1
doihttps://doi.org/10.1007/978-3-031-11698-8
isbn_softcover978-3-031-11697-1
isbn_ebook978-3-031-11698-8Series ISSN 2191-8198 Series E-ISSN 2191-8201
issn_series 2191-8198
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:26:33 | 只看該作者
Preliminaries,d this theorem as well as the algorithms that follow. We first review some basic ideas of graph theory and linear algebra. The necessary background on eigenvalues and eigenvectors is given, and we define what we mean by an .. In this book, the eigenvalues usually come from the adjacency matrix .(.),
板凳
發(fā)表于 2025-3-22 03:21:05 | 只看該作者
Locating Eigenvalues in Trees,llowed. For a rooted tree ., it can compute in linear time the number of eigenvalues that lie in any interval. It is simple enough to allow calculations by hand on small trees. A unique feature is that the algorithm operates bottom-up on a rooted tree, performing a diagonalization while storing diag
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發(fā)表于 2025-3-22 11:51:14 | 只看該作者
Locating Eigenvalues in Cographs,t will be presented in Chap. ., the clique-width algorithm. Our focus is on the adjacency matrix of cographs. As in the case of trees, the algorithm operates in linear time with respect to the number of vertices. It operates on the cotree of the cograph. As applications, we give a formula for the in
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發(fā)表于 2025-3-22 15:24:45 | 只看該作者
Distance-Hereditary Graphs,e trees. Trees and cographs are distance-hereditary. Like other graph classes discussed in earlier chapters, there exists a linear time diagonalization algorithm. When . is distance-hereditary, .(.)?≤?2. However, there exist graphs with .(.)?=?2 that are not distance-hereditary. Many results in this
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發(fā)表于 2025-3-22 18:21:26 | 只看該作者
Carlos Hoppen,David P. Jacobs,Vilmar TrevisanOffers 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
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Locating Eigenvalues in Graphs978-3-031-11698-8Series ISSN 2191-8198 Series E-ISSN 2191-8201
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