Graph Embeddings and Laplacian Eigenvalues

Graph Embeddings and Laplacian Eigenvalues
Author: Stephen Guattery
Publisher:
Total Pages: 26
Release: 1998
Genre: Embeddings (Mathematics)
ISBN:

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Abstract: "Graph embeddings are useful in bounding the smallest nontrivial eigenvalues of Laplacian matrices from below. For an n x n Laplacian, these embedding methods can be characterized as follows: The lower bound is based on a clique embedding into the underlying graph of the Laplacian. An embedding can be represented by a matrix [gamma]; the best possible bound based on this embedding is n/[lambda][subscript max]([gamma superscript T gamma]). However, the best bounds produced by embedding techniques are not tight; they can be off by a factor proportional to log2n for some Laplacians. We show that this gap is a result of the representation of the embedding: by including edge directions in the embedding matrix representation [gamma], it is possible to find an embedding such that [gamma superscript T gamma] has eigenvalues that can be put into a one-to-one correspondence with the eigenvalues of the Laplacian. Specifically, if [lambda] is a nonzero eigenvalue of either matrix, then n/[lambda] is an eigenvalue of the other. Simple transformations map the corresponding eigenvectors to each other. The embedding that produces these correspondences has a simple description in electrical terms if the underlying graph of the Laplaciain [sic] is viewed as a resistive circuit. We also show that a similar technique works for star embeddings when the Laplacian has a zero Dirichlet boundary condition, though the related eigenvalues in this case are reciprocals of each other. In the Dirichlet boundary case, the embedding matrix [gamma] can be used to construct the inverse of the Laplacian. Finally, we connect our results with previous techniques for producing bounds, and provide an illustrative example."

Laplacian Eigenvectors of Graphs

Laplacian Eigenvectors of Graphs
Author: Türker Biyikoglu
Publisher: Springer
Total Pages: 121
Release: 2007-07-07
Genre: Mathematics
ISBN: 3540735100

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This fascinating volume investigates the structure of eigenvectors and looks at the number of their sign graphs ("nodal domains"), Perron components, and graphs with extremal properties with respect to eigenvectors. The Rayleigh quotient and rearrangement of graphs form the main methodology. Eigenvectors of graph Laplacians may seem a surprising topic for a book, but the authors show that there are subtle differences between the properties of solutions of Schrödinger equations on manifolds on the one hand, and their discrete analogs on graphs.

Graph Embeddings, Symmetric Real Matrices, and Generalized Inverses

Graph Embeddings, Symmetric Real Matrices, and Generalized Inverses
Author: Stephen Guattery
Publisher:
Total Pages: 18
Release: 1998
Genre: Eigenvalues
ISBN:

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Graph embedding techniques for bounding eigenvalues of associated matrices have a wide range of applications. The bounds produced by these techniques are not in general tight, however, and may be off by a log(2)n factor for some graphs. Guattery and Miller showed that, by adding edge directions to the graph representation, they could construct an embedding called the current flow embedding, which embeds each edge of the guest graph as an electric current flow in the host graph. They also showed how this embedding can be used to construct matrices whose nonzero eigenvalues had a one-to-one correspondence to the reciprocals of the eigenvalues of the generalized Laplacians. For the Laplacians of graphs with zero Dirichlet boundary conditions, they showed that the current flow embedding could be used generate the inverse of the matrix. In this paper, we generalize the definition of graph embeddings to cover all symmetric matrices, and we show a way of computing a generalized current flow embedding. We prove that, for any symmetric matrix A, the generalized current flow embedding of the orthogonal projector for the column space of A into A can be used to construct the generalized inverse, or pseudoinverse, of A. We also show how these results can be extended to cover Hermitian matrices.

Inequalities for Graph Eigenvalues

Inequalities for Graph Eigenvalues
Author: Zoran Stanić
Publisher: Cambridge University Press
Total Pages: 311
Release: 2015-07-23
Genre: Mathematics
ISBN: 1316395758

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Written for mathematicians working with the theory of graph spectra, this book explores more than 400 inequalities for eigenvalues of the six matrices associated with finite simple graphs: the adjacency matrix, Laplacian matrix, signless Laplacian matrix, normalized Laplacian matrix, Seidel matrix, and distance matrix. The book begins with a brief survey of the main results and selected applications to related topics, including chemistry, physics, biology, computer science, and control theory. The author then proceeds to detail proofs, discussions, comparisons, examples, and exercises. Each chapter ends with a brief survey of further results. The author also points to open problems and gives ideas for further reading.

Graph Representation Learning

Graph Representation Learning
Author: William L. William L. Hamilton
Publisher: Springer Nature
Total Pages: 141
Release: 2022-06-01
Genre: Computers
ISBN: 3031015886

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Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Distribution of Laplacian Eigenvalues of Graphs

Distribution of Laplacian Eigenvalues of Graphs
Author: Bilal Ahmad Rather
Publisher: A.K. Publications
Total Pages: 0
Release: 2022-12-22
Genre: Mathematics
ISBN: 9783258974040

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Spectral graph theory (Algebraic graph theory) is the study of spectral properties of matrices associated to graphs. The spectral properties include the study of characteristic polynomial, eigenvalues and eigenvectors of matrices associated to graphs. This also includes the graphs associated to algebraic structures like groups, rings and vector spaces. The major source of research in spectral graph theory has been the study of relationship between the structural and spectral properties of graphs. Another source has research in mathematical chemistry (theoretical/quantum chemistry). One of the major problems in spectral graph theory lies in finding the spectrum of matrices associated to graphs completely or in terms of spectrum of simpler matrices associated with the structure of the graph. Another problem which is worth to mention is to characterise the extremal graphs among all the graphs or among a special class of graphs with respect to a given graph, like spectral radius, the second largest eigenvalue, the smallest eigenvalue, the second smallest eigenvalue, the graph energy and multiplicities of the eigenvalues that can be associated with the graph matrix. The main aim is to discuss the principal properties and structure of a graph from its eigenvalues. It has been observed that the eigenvalues of graphs are closely related to all graph parameters, linking one property to another. Spectral graph theory has a wide range of applications to other areas of mathematical science and to other areas of sciences which include Computer Science, Physics, Chemistry, Biology, Statistics, Engineering etc. The study of graph eigen- values has rich connections with many other areas of mathematics. An important development is the interaction between spectral graph theory and differential geometry. There is an interesting connection between spectral Riemannian geometry and spectral graph theory. Graph operations help in partitioning of the embedding space, maximising inter-cluster affinity and minimising inter-cluster proximity. Spectral graph theory plays a major role in deforming the embedding spaces in geometry. Graph spectra helps us in making conclusions that we cannot recognize the shapes of solids by their sounds. Algebraic spectral methods are also useful in studying the groups and the rings in a new light. This new developing field investigates the spectrum of graphs associated with the algebraic structures like groups and rings. The main motive to study these algebraic structures graphically using spectral analysis is to explore several properties of interest.

Graph Embedding for Pattern Analysis

Graph Embedding for Pattern Analysis
Author: Yun Fu
Publisher: Springer Science & Business Media
Total Pages: 264
Release: 2012-11-19
Genre: Technology & Engineering
ISBN: 1461444578

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Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

Lx = B - Laplacian Solvers and Their Algorithmic Applications

Lx = B - Laplacian Solvers and Their Algorithmic Applications
Author: Nisheeth K Vishnoi
Publisher:
Total Pages: 168
Release: 2013-03-01
Genre:
ISBN: 9781601986566

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Illustrates the emerging paradigm of employing Laplacian solvers to design novel fast algorithms for graph problems through a small but carefully chosen set of examples. This monograph can be used as the text for a graduate-level course, or act as a supplement to a course on spectral graph theory or algorithms.

Supervised Learning with Quantum Computers

Supervised Learning with Quantum Computers
Author: Maria Schuld
Publisher: Springer
Total Pages: 293
Release: 2018-08-30
Genre: Science
ISBN: 3319964240

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Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.