Dynamical Systems Associated with Adjacency Matrices
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Adjacency and Incidence Matrices
Adjacency and Incidence Matrices 1 / 10 The Incidence Matrix of a Graph Definition Let G = (V ; E) be a graph where V = f1; 2;:::; ng and E = fe1; e2;:::; emg. The incidence matrix of G is an n × m matrix B = (bik ), where each row corresponds to a vertex and each column corresponds to an edge such that if ek is an edge between i and j, then all elements of column k are 0 except bik = bjk = 1. 1 2 e 21 1 13 f 61 0 07 3 B = 6 7 g 40 1 05 4 0 0 1 2 / 10 The First Theorem of Graph Theory Theorem If G is a multigraph with no loops and m edges, the sum of the degrees of all the vertices of G is 2m. Corollary The number of odd vertices in a loopless multigraph is even. 3 / 10 Linear Algebra and Incidence Matrices of Graphs Recall that the rank of a matrix is the dimension of its row space. Proposition Let G be a connected graph with n vertices and let B be the incidence matrix of G. Then the rank of B is n − 1 if G is bipartite and n otherwise. Example 1 2 e 21 1 13 f 61 0 07 3 B = 6 7 g 40 1 05 4 0 0 1 4 / 10 Linear Algebra and Incidence Matrices of Graphs Recall that the rank of a matrix is the dimension of its row space. Proposition Let G be a connected graph with n vertices and let B be the incidence matrix of G. -
Graph Equivalence Classes for Spectral Projector-Based Graph Fourier Transforms Joya A
1 Graph Equivalence Classes for Spectral Projector-Based Graph Fourier Transforms Joya A. Deri, Member, IEEE, and José M. F. Moura, Fellow, IEEE Abstract—We define and discuss the utility of two equiv- Consider a graph G = G(A) with adjacency matrix alence graph classes over which a spectral projector-based A 2 CN×N with k ≤ N distinct eigenvalues and Jordan graph Fourier transform is equivalent: isomorphic equiv- decomposition A = VJV −1. The associated Jordan alence classes and Jordan equivalence classes. Isomorphic equivalence classes show that the transform is equivalent subspaces of A are Jij, i = 1; : : : k, j = 1; : : : ; gi, up to a permutation on the node labels. Jordan equivalence where gi is the geometric multiplicity of eigenvalue 휆i, classes permit identical transforms over graphs of noniden- or the dimension of the kernel of A − 휆iI. The signal tical topologies and allow a basis-invariant characterization space S can be uniquely decomposed by the Jordan of total variation orderings of the spectral components. subspaces (see [13], [14] and Section II). For a graph Methods to exploit these classes to reduce computation time of the transform as well as limitations are discussed. signal s 2 S, the graph Fourier transform (GFT) of [12] is defined as Index Terms—Jordan decomposition, generalized k gi eigenspaces, directed graphs, graph equivalence classes, M M graph isomorphism, signal processing on graphs, networks F : S! Jij i=1 j=1 s ! (s ;:::; s ;:::; s ;:::; s ) ; (1) b11 b1g1 bk1 bkgk I. INTRODUCTION where sij is the (oblique) projection of s onto the Jordan subspace Jij parallel to SnJij. -
Coloring ($ P 6 $, Diamond, $ K 4 $)-Free Graphs
Coloring (P6, diamond, K4)-free graphs T. Karthick∗ Suchismita Mishra† June 28, 2021 Abstract We show that every (P6, diamond, K4)-free graph is 6-colorable. Moreover, we give an example of a (P6, diamond, K4)-free graph G with χ(G) = 6. This generalizes some known results in the literature. 1 Introduction We consider simple, finite, and undirected graphs. For notation and terminology not defined here we refer to [22]. Let Pn, Cn, Kn denote the induced path, induced cycle and complete graph on n vertices respectively. If G1 and G2 are two vertex disjoint graphs, then their union G1 ∪ G2 is the graph with V (G1 ∪ G2) = V (G1) ∪ V (G2) and E(G1 ∪ G2) = E(G1) ∪ E(G2). Similarly, their join G1 + G2 is the graph with V (G1 + G2) = V (G1) ∪ V (G2) and E(G1 + G2) = E(G1) ∪ E(G2)∪{(x,y) | x ∈ V (G1), y ∈ V (G2)}. For any positive integer k, kG denotes the union of k graphs each isomorphic to G. If F is a family of graphs, a graph G is said to be F-free if it contains no induced subgraph isomorphic to any member of F. A clique (independent set) in a graph G is a set of vertices that are pairwise adjacent (non-adjacent) in G. The clique number of G, denoted by ω(G), is the size of a maximum clique in G. A k-coloring of a graph G = (V, E) is a mapping f : V → {1, 2,...,k} such that f(u) 6= f(v) whenever uv ∈ E. -
Network Properties Revealed Through Matrix Functions 697
SIAM REVIEW c 2010 Society for Industrial and Applied Mathematics Vol. 52, No. 4, pp. 696–714 Network Properties Revealed ∗ through Matrix Functions † Ernesto Estrada Desmond J. Higham‡ Abstract. The emerging field of network science deals with the tasks of modeling, comparing, and summarizing large data sets that describe complex interactions. Because pairwise affinity data can be stored in a two-dimensional array, graph theory and applied linear algebra provide extremely useful tools. Here, we focus on the general concepts of centrality, com- municability,andbetweenness, each of which quantifies important features in a network. Some recent work in the mathematical physics literature has shown that the exponential of a network’s adjacency matrix can be used as the basis for defining and computing specific versions of these measures. We introduce here a general class of measures based on matrix functions, and show that a particular case involving a matrix resolvent arises naturally from graph-theoretic arguments. We also point out connections between these measures and the quantities typically computed when spectral methods are used for data mining tasks such as clustering and ordering. We finish with computational examples showing the new matrix resolvent version applied to real networks. Key words. centrality measures, clustering methods, communicability, Estrada index, Fiedler vector, graph Laplacian, graph spectrum, power series, resolvent AMS subject classifications. 05C50, 05C82, 91D30 DOI. 10.1137/090761070 1. Motivation. 1.1. Introduction. Connections are important. Across the natural, technologi- cal, and social sciences it often makes sense to focus on the pattern of interactions be- tween individual components in a system [1, 8, 61]. -
Diagonal Sums of Doubly Stochastic Matrices Arxiv:2101.04143V1 [Math
Diagonal Sums of Doubly Stochastic Matrices Richard A. Brualdi∗ Geir Dahl† 7 December 2020 Abstract Let Ωn denote the class of n × n doubly stochastic matrices (each such matrix is entrywise nonnegative and every row and column sum is 1). We study the diagonals of matrices in Ωn. The main question is: which A 2 Ωn are such that the diagonals in A that avoid the zeros of A all have the same sum of their entries. We give a characterization of such matrices, and establish several classes of patterns of such matrices. Key words. Doubly stochastic matrix, diagonal sum, patterns. AMS subject classifications. 05C50, 15A15. 1 Introduction Let Mn denote the (vector) space of real n×n matrices and on this space we consider arXiv:2101.04143v1 [math.CO] 11 Jan 2021 P the usual scalar product A · B = i;j aijbij for A; B 2 Mn, A = [aij], B = [bij]. A permutation σ = (k1; k2; : : : ; kn) of f1; 2; : : : ; ng can be identified with an n × n permutation matrix P = Pσ = [pij] by defining pij = 1, if j = ki, and pij = 0, otherwise. If X = [xij] is an n × n matrix, the entries of X in the positions of X in ∗Department of Mathematics, University of Wisconsin, Madison, WI 53706, USA. [email protected] †Department of Mathematics, University of Oslo, Norway. [email protected]. Correspond- ing author. 1 which P has a 1 is the diagonal Dσ of X corresponding to σ and P , and their sum n X dP (X) = xi;ki i=1 is a diagonal sum of X. -
Isometric Diamond Subgraphs
Isometric Diamond Subgraphs David Eppstein Computer Science Department, University of California, Irvine [email protected] Abstract. We test in polynomial time whether a graph embeds in a distance- preserving way into the hexagonal tiling, the three-dimensional diamond struc- ture, or analogous higher-dimensional structures. 1 Introduction Subgraphs of square or hexagonal tilings of the plane form nearly ideal graph draw- ings: their angular resolution is bounded, vertices have uniform spacing, all edges have unit length, and the area is at most quadratic in the number of vertices. For induced sub- graphs of these tilings, one can additionally determine the graph from its vertex set: two vertices are adjacent whenever they are mutual nearest neighbors. Unfortunately, these drawings are hard to find: it is NP-complete to test whether a graph is a subgraph of a square tiling [2], a planar nearest-neighbor graph, or a planar unit distance graph [5], and Eades and Whitesides’ logic engine technique can also be used to show the NP- completeness of determining whether a given graph is a subgraph of the hexagonal tiling or an induced subgraph of the square or hexagonal tilings. With stronger constraints on subgraphs of tilings, however, they are easier to con- struct: one can test efficiently whether a graph embeds isometrically onto the square tiling, or onto an integer grid of fixed or variable dimension [7]. In an isometric em- bedding, the unweighted distance between any two vertices in the graph equals the L1 distance of their placements in the grid. An isometric embedding must be an induced subgraph, but not all induced subgraphs are isometric. -
Graphs with Few Trivial Characteristic Ideals
University of Rhode Island DigitalCommons@URI Mathematics Faculty Publications Mathematics 2021 Graphs with few trivial characteristic ideals Carlos A. Alfaro Michael D. Barrus John Sinkovic Ralihe R. Villagrán Follow this and additional works at: https://digitalcommons.uri.edu/math_facpubs The University of Rhode Island Faculty have made this article openly available. Please let us know how Open Access to this research benefits you. This is a pre-publication author manuscript of the final, published article. Terms of Use This article is made available under the terms and conditions applicable towards Open Access Policy Articles, as set forth in our Terms of Use. Graphs with few trivial characteristic ideals Carlos A. Alfaroa, Michael D. Barrusb, John Sinkovicc and Ralihe R. Villagr´and a Banco de M´exico Mexico City, Mexico [email protected] bDepartment of Mathematics University of Rhode Island Kingston, RI 02881, USA [email protected] cDepartment of Mathematics Brigham Young University - Idaho Rexburg, ID 83460, USA [email protected] dDepartamento de Matem´aticas Centro de Investigaci´ony de Estudios Avanzados del IPN Apartado Postal 14-740, 07000 Mexico City, Mexico [email protected] Abstract We give a characterization of the graphs with at most three trivial characteristic ideals. This implies the complete characterization of the regular graphs whose critical groups have at most three invariant factors equal to 1 and the characterization of the graphs whose Smith groups have at most 3 invariant factors equal to 1. We also give an alternative and simpler way to obtain the characterization of the graphs whose Smith groups have at most 3 invariant factors equal to 1, and a list of minimal forbidden graphs for the family of graphs with Smith group having at most 4 invariant factors equal to 1. -
The Generalized Dedekind Determinant
Contemporary Mathematics Volume 655, 2015 http://dx.doi.org/10.1090/conm/655/13232 The Generalized Dedekind Determinant M. Ram Murty and Kaneenika Sinha Abstract. The aim of this note is to calculate the determinants of certain matrices which arise in three different settings, namely from characters on finite abelian groups, zeta functions on lattices and Fourier coefficients of normalized Hecke eigenforms. Seemingly disparate, these results arise from a common framework suggested by elementary linear algebra. 1. Introduction The purpose of this note is three-fold. We prove three seemingly disparate results about matrices which arise in three different settings, namely from charac- ters on finite abelian groups, zeta functions on lattices and Fourier coefficients of normalized Hecke eigenforms. In this section, we state these theorems. In Section 2, we state a lemma from elementary linear algebra, which lies at the heart of our three theorems. A detailed discussion and proofs of the theorems appear in Sections 3, 4 and 5. In what follows below, for any n × n matrix A and for 1 ≤ i, j ≤ n, Ai,j or (A)i,j will denote the (i, j)-th entry of A. A diagonal matrix with diagonal entries y1,y2, ...yn will be denoted as diag (y1,y2, ...yn). Theorem 1.1. Let G = {x1,x2,...xn} be a finite abelian group and let f : G → C be a complex-valued function on G. Let F be an n × n matrix defined by F −1 i,j = f(xi xj). For a character χ on G, (that is, a homomorphism of G into the multiplicative group of the field C of complex numbers), we define Sχ := f(s)χ(s). -
Approximation Algorithms for Problems in Makespan Minimization on Unrelated Parallel Machines
Western University Scholarship@Western Electronic Thesis and Dissertation Repository 4-8-2019 10:30 AM Approximation Algorithms for Problems in Makespan Minimization on Unrelated Parallel Machines Daniel R. Page The University of Western Ontario Supervisor Solis-Oba, Roberto The University of Western Ontario Graduate Program in Computer Science A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Daniel R. Page 2019 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Discrete Mathematics and Combinatorics Commons, and the Theory and Algorithms Commons Recommended Citation Page, Daniel R., "Approximation Algorithms for Problems in Makespan Minimization on Unrelated Parallel Machines" (2019). Electronic Thesis and Dissertation Repository. 6109. https://ir.lib.uwo.ca/etd/6109 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. Abstract A fundamental problem in scheduling is makespan minimization on unrelated parallel ma- chines (RjjCmax). Let there be a set J of jobs and a set M of parallel machines, where every + job J j 2 J has processing time or length pi; j 2 Q on machine Mi 2 M. The goal in RjjCmax is to schedule the jobs non-preemptively on the machines so as to minimize the length of the schedule, the makespan. A ρ-approximation algorithm produces in polynomial time a feasible solution such that its objective value is within a multiplicative factor ρ of the optimum, where ρ is called its approximation ratio. -
Adjacency Matrices Text Reference: Section 2.1, P
Adjacency Matrices Text Reference: Section 2.1, p. 114 The purpose of this set of exercises is to show how powers of a matrix may be used to investigate graphs. Special attention is paid to airline route maps as examples of graphs. In order to study graphs, the notion of graph must first be defined. A graph is a set of points (called vertices,ornodes) and a set of lines called edges connecting some pairs of vertices. Two vertices connected by an edge are said to be adjacent. Consider the graph in Figure 1. Notice that two vertices may be connected by more than one edge (A and B are connected by 2 distinct edges), that a vertex need not be connected to any other vertex (D), and that a vertex may be connected to itself (F). Another example of a graph is the route map that most airlines (or railways) produce. A copy of the northern route map for Cape Air from May 2001 is Figure 2. This map is available at the web page: www.flycapeair.com. Here the vertices are the cities to which Cape Air flies, and two vertices are connected if a direct flight flies between them. Figure 2: Northern Route Map for Cape Air -- May 2001 Application Project: Adjacency Matrices Page 2 of 5 Some natural questions arise about graphs. It might be important to know if two vertices are connected by a sequence of two edges, even if they are not connected by a single edge. In Figure 1, A and C are connected by a two-edge sequence (actually, there are four distinct ways to go from A to C in two steps). -
On Structured Realizability and Stabilizability of Linear Systems
On Structured Realizability and Stabilizability of Linear Systems Laurent Lessard Maxim Kristalny Anders Rantzer Abstract We study the notion of structured realizability for linear systems defined over graphs. A stabilizable and detectable realization is structured if the state-space matrices inherit the sparsity pattern of the adjacency matrix of the associated graph. In this paper, we demonstrate that not every structured transfer matrix has a structured realization and we reveal the practical meaning of this fact. We also uncover a close connection between the structured realizability of a plant and whether the plant can be stabilized by a structured controller. In particular, we show that a structured stabilizing controller can only exist when the plant admits a structured realization. Finally, we give a parameterization of all structured stabilizing controllers and show that they always have structured realizations. 1 Introduction Linear time-invariant systems are typically represented using transfer functions or state- space realizations. The relationship between these representations is well understood; every transfer function has a minimal state-space realization and one can easily move between representations depending on the need. In this paper, we address the question of whether this relationship between representa- tions still holds for systems defined over directed graphs. Consider the simple two-node graph of Figure 1. For i = 1, 2, the node i represents a subsystem with inputs ui and outputs yi, and the edge means that subsystem 1 can influence subsystem 2 but not vice versa. The transfer matrix for any such a system has the sparsity pattern S1. For a state-space realization to make sense in this context, every state should be as- sociated with a subsystem, which means that the state should be computable using the information available to that subsystem. -
A Note on the Spectra and Eigenspaces of the Universal Adjacency Matrices of Arbitrary Lifts of Graphs
A note on the spectra and eigenspaces of the universal adjacency matrices of arbitrary lifts of graphs C. Dalf´o,∗ M. A. Fiol,y S. Pavl´ıkov´a,z and J. Sir´aˇnˇ x Abstract The universal adjacency matrix U of a graph Γ, with adjacency matrix A, is a linear combination of A, the diagonal matrix D of vertex degrees, the identity matrix I, and the all-1 matrix J with real coefficients, that is, U = c1A + c2D + c3I + c4J, with ci 2 R and c1 6= 0. Thus, as particular cases, U may be the adjacency matrix, the Laplacian, the signless Laplacian, and the Seidel matrix. In this note, we show that basically the same method introduced before by the authors can be applied for determining the spectra and bases of all the corresponding eigenspaces of arbitrary lifts of graphs (regular or not). Keywords: Graph; covering; lift; universal adjacency matrix; spectrum; eigenspace. 2010 Mathematics Subject Classification: 05C20, 05C50, 15A18. 1 Introduction For a graph Γ on n vertices, with adjacency matrix A and degree sequence d1; : : : ; dn, the universal adjacency matrix is defined as U = c1A+c2D +c3I +c4J, with ci 2 R and c1 6= 0, where D = diag(d1; : : : ; dn), I is the identity matrix, and J is the all-1 matrix. See, for arXiv:1912.04740v1 [math.CO] 8 Dec 2019 instance, Haemers and Omidi [10] or Farrugia and Sciriha [5]. Thus, for given values of the ∗Departament de Matem`atica, Universitat de Lleida, Igualada (Barcelona), Catalonia, [email protected] yDepartament de Matem`atiques,Universitat Polit`ecnicade Catalunya; and Barcelona Graduate