Three Conjectures in Extremal Spectral Graph Theory
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Manifold Learning
MANIFOLD LEARNING Kavé Salamatian- LISTIC Learning Machine Learning: develop algorithms to automatically extract ”patterns” or ”regularities” from data (generalization) Typical tasks Clustering: Find groups of similar points Dimensionality reduction: Project points in a lower dimensional space while preserving structure Semi-supervised: Given labelled and unlabelled points, build a labelling function Supervised: Given labelled points, build a labelling function All these tasks are not well-defined Clustering Identify the two groups Dimensionality Reduction 4096 Objects in R ? But only 3 parameters: 2 angles and 1 for illumination. They span a 3- dimensional sub-manifold of R4096! Semi-Supervised Learning Assign labels to black points Supervised learning Build a function that predicts the label of all points in the space Formal definitions Clustering: Given build a function Dimensionality reduction: Given build a function Semi-supervised: Given and with m << n, build a function Supervised: Given , build Geometric learning Consider and exploit the geometric structure of the data Clustering: two ”close” points should be in the same cluster Dimensionality reduction: ”closeness” should be preserved Semi-supervised/supervised: two ”close” points should have the same label Examples: k-means clustering, k-nearest neighbors. Manifold ? A manifold is a topological space that is locally Euclidian Around every point, there is a neighborhood that is topologically isomorph to unit ball Regularization Adopt a functional viewpoint -
Forbidding Subgraphs
Graph Theory and Additive Combinatorics Lecturer: Prof. Yufei Zhao 2 Forbidding subgraphs 2.1 Mantel’s theorem: forbidding a triangle We begin our discussion of extremal graph theory with the following basic question. Question 2.1. What is the maximum number of edges in an n-vertex graph that does not contain a triangle? Bipartite graphs are always triangle-free. A complete bipartite graph, where the vertex set is split equally into two parts (or differing by one vertex, in case n is odd), has n2/4 edges. Mantel’s theorem states that we cannot obtain a better bound: Theorem 2.2 (Mantel). Every triangle-free graph on n vertices has at W. Mantel, "Problem 28 (Solution by H. most bn2/4c edges. Gouwentak, W. Mantel, J. Teixeira de Mattes, F. Schuh and W. A. Wythoff). Wiskundige Opgaven 10, 60 —61, 1907. We will give two proofs of Theorem 2.2. Proof 1. G = (V E) n m Let , a triangle-free graph with vertices and x edges. Observe that for distinct x, y 2 V such that xy 2 E, x and y N(x) must not share neighbors by triangle-freeness. Therefore, d(x) + d(y) ≤ n, which implies that d(x)2 = (d(x) + d(y)) ≤ mn. ∑ ∑ N(y) x2V xy2E y On the other hand, by the handshake lemma, ∑x2V d(x) = 2m. Now by the Cauchy–Schwarz inequality and the equation above, Adjacent vertices have disjoint neigh- borhoods in a triangle-free graph. !2 ! 4m2 = ∑ d(x) ≤ n ∑ d(x)2 ≤ mn2; x2V x2V hence m ≤ n2/4. -
Extremal Graph Theory
Extremal Graph Theory Ajit A. Diwan Department of Computer Science and Engineering, I. I. T. Bombay. Email: [email protected] Basic Question • Let H be a fixed graph. • What is the maximum number of edges in a graph G with n vertices that does not contain H as a subgraph? • This number is denoted ex(n,H). • A graph G with n vertices and ex(n,H) edges that does not contain H is called an extremal graph for H. Mantel’s Theorem (1907) n2 ex(n, K ) 3 4 • The only extremal graph for a triangle is the complete bipartite graph with parts of nearly equal sizes. Complete Bipartite graph Turan’s theorem (1941) t 2 ex(n, K ) n2 t 2(t 1) • Equality holds when n is a multiple of t-1. • The only extremal graph is the complete (t-1)- partite graph with parts of nearly equal sizes. Complete Multipartite Graph Proofs of Turan’s theorem • Many different proofs. • Use different techniques. • Techniques useful in proving other results. • Algorithmic applications. • “BOOK” proofs. Induction • The result is trivial if n <= t-1. • Suppose n >= t and consider a graph G with maximum number of edges and no Kt. • G must contain a Kt-1. • Delete all vertices in Kt-1. • The remaining graph contains at most t 2 (n t 1)2 edges. 2(t 1) Induction • No vertex outside Kt-1 can be joined to all vertices of Kt-1. • Total number of edges is at most t 2 (t 1)(t 2) (n t 1)2 2(t 1) 2 t 2 (n t 1)(t 2) n2 2(t 1) Greedy algorithm • Consider any extremal graph and let v be a vertex with maximum degree ∆. -
An Extremal Problem for Complete Bipartite Graphs
Studia Seientiarum Mathematicarum Hungarica 23 (1988), 319-326 AN EXTREMAL PROBLEM FOR COMPLETE BIPARTITE GRAPHS P. ERDŐS, R. J. FAUDREE, C . C . ROUSSEAU and R . H. SCHELP Dedicated to the memory of Paul Turán Abstract Define f(n, k) to be the largest integer q such that for every graph G of order n and size q, G contains every complete bipartite graph K u, ,, with a+h=n-k . We obtain (i) exact values for f(n, 0) and f(n, 1), (ii) upper and lower bounds for f(n, k) when ku2 is fixed and n is large, and (iii) an upper bound for f(n, lenl) . 1 . Introduction Extremal graph theory, which was initiated by Turán in 1941 [4], is still the source of many interesting and difficult problems . The standard problem is to deter- mine f(n, G), the smallest integer q such that every graph with n vertices and q edges contains a subgraph isomorphic to G . It is striking that whereas Turán completely determined f(n, Km), there is much which is as yet unknown concerning f(n, Ka, b). In this paper, we consider a variant of the extremal problem for complete bipartite graphs . In this variant we ask how many edges must be deleted from K„ so that the resulting graph no longer contains Ka,, for some pair (a, b) with a+b=in . Specifically, we seek to determine an extremal function f(n, k) defined as follows . For in ::- 1, let B,,, denote the class of all graphs G such that GDKa , b for every pair (a, b) with a+b=m . -
The History of Degenerate (Bipartite) Extremal Graph Problems
The history of degenerate (bipartite) extremal graph problems Zolt´an F¨uredi and Mikl´os Simonovits May 15, 2013 Alfr´ed R´enyi Institute of Mathematics, Budapest, Hungary [email protected] and [email protected] Abstract This paper is a survey on Extremal Graph Theory, primarily fo- cusing on the case when one of the excluded graphs is bipartite. On one hand we give an introduction to this field and also describe many important results, methods, problems, and constructions. 1 Contents 1 Introduction 4 1.1 Some central theorems of the field . 5 1.2 Thestructureofthispaper . 6 1.3 Extremalproblems ........................ 8 1.4 Other types of extremal graph problems . 10 1.5 Historicalremarks . .. .. .. .. .. .. 11 arXiv:1306.5167v2 [math.CO] 29 Jun 2013 2 The general theory, classification 12 2.1 The importance of the Degenerate Case . 14 2.2 The asymmetric case of Excluded Bipartite graphs . 15 2.3 Reductions:Hostgraphs. 16 2.4 Excluding complete bipartite graphs . 17 2.5 Probabilistic lower bound . 18 1 Research supported in part by the Hungarian National Science Foundation OTKA 104343, and by the European Research Council Advanced Investigators Grant 267195 (ZF) and by the Hungarian National Science Foundation OTKA 101536, and by the European Research Council Advanced Investigators Grant 321104. (MS). 1 F¨uredi-Simonovits: Degenerate (bipartite) extremal graph problems 2 2.6 Classification of extremal problems . 21 2.7 General conjectures on bipartite graphs . 23 3 Excluding complete bipartite graphs 24 3.1 Bipartite C4-free graphs and the Zarankiewicz problem . 24 3.2 Finite Geometries and the C4-freegraphs . -
Contemporary Spectral Graph Theory
CONTEMPORARY SPECTRAL GRAPH THEORY GUEST EDITORS Maurizio Brunetti, University of Naples Federico II, Italy, [email protected] Raffaella Mulas, The Alan Turing Institute, UK, [email protected] Lucas Rusnak, Texas State University, USA, [email protected] Zoran Stanić, University of Belgrade, Serbia, [email protected] DESCRIPTION Spectral graph theory is a striking theory that studies the properties of a graph in relationship to the characteristic polynomial, eigenvalues and eigenvectors of matrices associated with the graph, such as the adjacency matrix, the Laplacian matrix, the signless Laplacian matrix or the distance matrix. This theory emerged in the 1950s and 1960s. Over the decades it has considered not only simple undirected graphs but also their generalizations such as multigraphs, directed graphs, weighted graphs or hypergraphs. In the recent years, spectra of signed graphs and gain graphs have attracted a great deal of attention. Besides graph theoretic research, another major source is the research in a wide branch of applications in chemistry, physics, biology, electrical engineering, social sciences, computer sciences, information theory and other disciplines. This thematic special issue is devoted to the publication of original contributions focusing on the spectra of graphs or their generalizations, along with the most recent applications. Contributions to the Special Issue may address (but are not limited) to the following aspects: f relations between spectra of graphs (or their generalizations) and their structural properties in the most general shape, f bounds for particular eigenvalues, f spectra of particular types of graph, f relations with block designs, f particular eigenvalues of graphs, f cospectrality of graphs, f graph products and their eigenvalues, f graphs with a comparatively small number of eigenvalues, f graphs with particular spectral properties, f applications, f characteristic polynomials. -
Learning on Hypergraphs: Spectral Theory and Clustering
Learning on Hypergraphs: Spectral Theory and Clustering Pan Li, Olgica Milenkovic Coordinated Science Laboratory University of Illinois at Urbana-Champaign March 12, 2019 Learning on Graphs Graphs are indispensable mathematical data models capturing pairwise interactions: k-nn network social network publication network Important learning on graphs problems: clustering (community detection), semi-supervised/active learning, representation learning (graph embedding) etc. Beyond Pairwise Relations A graph models pairwise relations. Recent work has shown that high-order relations can be significantly more informative: Examples include: Understanding the organization of networks (Benson, Gleich and Leskovec'16) Determining the topological connectivity between data points (Zhou, Huang, Sch}olkopf'07). Graphs with high-order relations can be modeled as hypergraphs (formally defined later). Meta-graphs, meta-paths in heterogeneous information networks. Algorithmic methods for analyzing high-order relations and learning problems are still under development. Beyond Pairwise Relations Functional units in social and biological networks. High-order network motifs: Motif (Benson’16) Microfauna Pelagic fishes Crabs & Benthic fishes Macroinvertebrates Algorithmic methods for analyzing high-order relations and learning problems are still under development. Beyond Pairwise Relations Functional units in social and biological networks. Meta-graphs, meta-paths in heterogeneous information networks. (Zhou, Yu, Han'11) Beyond Pairwise Relations Functional units in social and biological networks. Meta-graphs, meta-paths in heterogeneous information networks. Algorithmic methods for analyzing high-order relations and learning problems are still under development. Review of Graph Clustering: Notation and Terminology Graph Clustering Task: Cluster the vertices that are \densely" connected by edges. Graph Partitioning and Conductance A (weighted) graph G = (V ; E; w): for e 2 E, we is the weight. -
Spectral Geometry for Structural Pattern Recognition
Spectral Geometry for Structural Pattern Recognition HEWAYDA EL GHAWALBY Ph.D. Thesis This thesis is submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy. Department of Computer Science United Kingdom May 2011 Abstract Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis of data in this form has proved an elusive problem; for instance, it suffers from the robustness to structural noise. One way to circumvent this problem is to embed the nodes of a graph in a vector space and to study the properties of the point distribution that results from the embedding. This is a problem that arises in a number of areas including manifold learning theory and graph-drawing. In this thesis, our first contribution is to investigate the heat kernel embed- ding as a route to computing geometric characterisations of graphs. The reason for turning to the heat kernel is that it encapsulates information concerning the distribution of path lengths and hence node affinities on the graph. The heat ker- nel of the graph is found by exponentiating the Laplacian eigensystem over time. The matrix of embedding co-ordinates for the nodes of the graph is obtained by performing a Young-Householder decomposition on the heat kernel. Once the embedding of its nodes is to hand we proceed to characterise a graph in a geometric manner. With the embeddings to hand, we establish a graph character- ization based on differential geometry by computing sets of curvatures associated ii Abstract iii with the graph nodes, edges and triangular faces. -
CS 598: Spectral Graph Theory. Lecture 10
CS 598: Spectral Graph Theory. Lecture 13 Expander Codes Alexandra Kolla Today Graph approximations, part 2. Bipartite expanders as approximations of the bipartite complete graph Quasi-random properties of bipartite expanders, expander mixing lemma Building expander codes Encoding, minimum distance, decoding Expander Graph Refresher We defined expander graphs to be d- regular graphs whose adjacency matrix eigenvalues satisfy |훼푖| ≤ 휖푑 for i>1, and some small 휖. We saw that such graphs are very good approximations of the complete graph. Expanders as Approximations of the Complete Graph Refresher Let G be a d-regular graph whose adjacency eigenvalues satisfy |훼푖| ≤ 휖푑. As its Laplacian eigenvalues satisfy 휆푖 = 푑 − 훼푖, all non-zero eigenvalues are between (1 − ϵ)d and 1 + ϵ 푑. 푇 푇 Let H=(d/n)Kn, 푠표 푥 퐿퐻푥 = 푑푥 푥 We showed that G is an ϵ − approximation of H 푇 푇 푇 1 − ϵ 푥 퐿퐻푥 ≼ 푥 퐿퐺푥 ≼ 1 + ϵ 푥 퐿퐻푥 Bipartite Expander Graphs Define them as d-regular good approximations of (some multiple of) the complete bipartite 푑 graph H= 퐾 : 푛 푛,푛 푇 푇 푇 1 − ϵ 푥 퐿퐻푥 ≼ 푥 퐿퐺푥 ≼ 1 + ϵ 푥 퐿퐻푥 ⇒ 1 − ϵ 퐻 ≼ 퐺 ≼ 1 + ϵ 퐻 G 퐾푛,푛 U V U V The Spectrum of Bipartite Expanders 푑 Eigenvalues of Laplacian of H= 퐾 are 푛 푛,푛 휆1 = 0, 휆2푛 = 2푑, 휆푖 = 푑, 0 < 푖 < 2푛 푇 푇 푇 1 − ϵ 푥 퐿퐻푥 ≼ 푥 퐿퐺푥 ≼ 1 + ϵ 푥 퐿퐻푥 Says we want d-regular graph G that has evals 휆1 = 0, 휆2푛 = 2푑, 휆푖 ∈ ( 1 − 휖 푑, 1 + 휖 푑), 0 < 푖 < 2푛 Construction of Bipartite Expanders Definition (Double Cover). -
Notes on Elementary Spectral Graph Theory Applications to Graph Clustering Using Normalized Cuts
University of Pennsylvania ScholarlyCommons Technical Reports (CIS) Department of Computer & Information Science 1-1-2013 Notes on Elementary Spectral Graph Theory Applications to Graph Clustering Using Normalized Cuts Jean H. Gallier University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/cis_reports Part of the Computer Engineering Commons, and the Computer Sciences Commons Recommended Citation Jean H. Gallier, "Notes on Elementary Spectral Graph Theory Applications to Graph Clustering Using Normalized Cuts", . January 2013. University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-13-09. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_reports/986 For more information, please contact [email protected]. Notes on Elementary Spectral Graph Theory Applications to Graph Clustering Using Normalized Cuts Abstract These are notes on the method of normalized graph cuts and its applications to graph clustering. I provide a fairly thorough treatment of this deeply original method due to Shi and Malik, including complete proofs. I include the necessary background on graphs and graph Laplacians. I then explain in detail how the eigenvectors of the graph Laplacian can be used to draw a graph. This is an attractive application of graph Laplacians. The main thrust of this paper is the method of normalized cuts. I give a detailed account for K = 2 clusters, and also for K > 2 clusters, based on the work of Yu and Shi. Three points that do not appear to have been clearly articulated before are elaborated: 1. The solutions of the main optimization problem should be viewed as tuples in the K-fold cartesian product of projective space RP^{N-1}. -
1 Turán's Theorem
Yuval Wigderson Turandotdotdot January 27, 2020 Ma il mio mistero `echiuso in me Nessun dorma, from Puccini's Turandot Libretto: G. Adami and R. Simoni 1 Tur´an'stheorem How many edges can we place among n vertices in such a way that we make no triangle? This is perhaps the first question asked in the field of extremal graph theory, which is the topic of this talk. Upon some experimentation, one can make the conjecture that the best thing to do is to split the vertices into two classes, of sizes x and n − x, and then connect any two vertices in different classes. This will not contain a triangle, since by the pigeonhole principle, among any three vertices there will be two in the same class, which will not be adjacent. This construction will have x(n − x) edges, and by the AM-GM inequality, we see that this is maximized when x and n − x are as close as possible; namely, our classes should n n n n n2 1 n have sizes b 2 c and d 2 e. Thus, this graph will have b 2 cd 2 e ≈ 4 ≈ 2 2 edges. Indeed, this is the unique optimal construction, as was proved by Mantel in 1907. In 1941, Tur´anconsidered a natural extension of this problem, where we forbid not a triangle, but instead a larger complete graph Kr+1. As before, a natural construction is to split the vertices into r almost-equal classes and connect all pairs of vertices in different classes. Definition 1. -
Extremal Graph Problems, Degenerate Extremal Problems, and Supersaturated Graphs
Extremal Graph Problems, Degenerate Extremal Problems, and Supersaturated Graphs Mikl´os Simonovits Aug, 2001 This is a LATEX version of my paper, from “Progress in Graph Theory”, the Waterloo Silver Jubilee Converence Proceedings, eds. Bondy and Murty, with slight changes in the notations, and some slight corrections. A Survey of Old and New Results Notation. Given a graph, hypergraph Gn, . , the upper index always de- notes the number of vertices, e(G), v(G) and χ(G) denote the number of edges, vertices and the chromatic number of G respectively. Given a family of graphs, hypergraphs, ex(n, ) denotes the maximum number of edges L L (hyperedges) a graph (hypergraph) Gn of order n can have without containing subgraphs (subhypergraphs) from . The problem of determining ex(n, ) is called a Tur´an-type extremal problemL . The graphs attaining the maximumL will be called extremal and their family will be denoted by EX(n, ). L 1 1. Introduction Let us restrict our consideration to ordinary graphs without loops and multi- ple edges. In 1940, P. Tur´an posed and solved the extremal problem of Kp+1, the complete graph on p + 1 vertices [39, 40]: ´ TURAN THEOREM. If Tn,p denotes the complete p–partite graph of order n having the maximum number of edges (or, in other words, the graph obtained by partitioning n vertices into p classes as equally as possible, and then joining two vertices iff they belong to different classes), then (a) If Tn,p contains no Kp+1, and (b) all the other graphs Gn of order n not containing Kp+1 have less than e(Tn,p) edges.