Layouts of Expander Graphs
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Expander Graphs
4 Expander Graphs Now that we have seen a variety of basic derandomization techniques, we will move on to study the first major “pseudorandom object” in this survey, expander graphs. These are graphs that are “sparse” yet very “well-connected.” 4.1 Measures of Expansion We will typically interpret the properties of expander graphs in an asymptotic sense. That is, there will be an infinite family of graphs Gi, with a growing number of vertices Ni. By “sparse,” we mean that the degree Di of Gi should be very slowly growing as a function of Ni. The “well-connectedness” property has a variety of different interpretations, which we will discuss below. Typically, we will drop the subscripts of i and the fact that we are talking about an infinite family of graphs will be implicit in our theorems. As in Section 2.4.2, we will state many of our definitions for directed multigraphs (which we’ll call digraphs for short), though in the end we will mostly study undirected multigraphs. 80 4.1 Measures of Expansion 81 4.1.1 Vertex Expansion The classic measure of well-connectedness in expanders requires that every “not-too-large” set of vertices has “many” neighbors: Definition 4.1. A digraph G isa(K,A) vertex expander if for all sets S of at most K vertices, the neighborhood N(S) def= {u|∃v ∈ S s.t. (u,v) ∈ E} is of size at least A ·|S|. Ideally, we would like D = O(1), K =Ω(N), where N is the number of vertices, and A as close to D as possible. -
Arxiv:1711.08757V3 [Cs.CV] 26 Jul 2018 1 Introduction
Deep Expander Networks: Efficient Deep Networks from Graph Theory Ameya Prabhu? Girish Varma? Anoop Namboodiri Center for Visual Information Technology Kohli Center on Intelligent Systems, IIIT Hyderabad, India [email protected], fgirish.varma, [email protected] https://github.com/DrImpossible/Deep-Expander-Networks Abstract. Efficient CNN designs like ResNets and DenseNet were pro- posed to improve accuracy vs efficiency trade-offs. They essentially in- creased the connectivity, allowing efficient information flow across layers. Inspired by these techniques, we propose to model connections between filters of a CNN using graphs which are simultaneously sparse and well connected. Sparsity results in efficiency while well connectedness can pre- serve the expressive power of the CNNs. We use a well-studied class of graphs from theoretical computer science that satisfies these properties known as Expander graphs. Expander graphs are used to model con- nections between filters in CNNs to design networks called X-Nets. We present two guarantees on the connectivity of X-Nets: Each node influ- ences every node in a layer in logarithmic steps, and the number of paths between two sets of nodes is proportional to the product of their sizes. We also propose efficient training and inference algorithms, making it possible to train deeper and wider X-Nets effectively. Expander based models give a 4% improvement in accuracy on Mo- bileNet over grouped convolutions, a popular technique, which has the same sparsity but worse connectivity. X-Nets give better performance trade-offs than the original ResNet and DenseNet-BC architectures. We achieve model sizes comparable to state-of-the-art pruning techniques us- ing our simple architecture design, without any pruning. -
Expander Graphs
Basic Facts about Expander Graphs Oded Goldreich Abstract. In this survey we review basic facts regarding expander graphs that are most relevant to the theory of computation. Keywords: Expander Graphs, Random Walks on Graphs. This text has been revised based on [8, Apdx. E.2]. 1 Introduction Expander graphs found numerous applications in the theory of computation, ranging from the design of sorting networks [1] to the proof that undirected connectivity is decidable in determinstic log-space [15]. In this survey we review basic facts regarding expander graphs that are most relevant to the theory of computation. For a wider perspective, the interested reader is referred to [10]. Loosely speaking, expander graphs are regular graphs of small degree that exhibit various properties of cliques.1 In particular, we refer to properties such as the relative sizes of cuts in the graph (i.e., relative to the number of edges), and the rate at which a random walk converges to the uniform distribution (relative to the logarithm of the graph size to the base of its degree). Some technicalities. Typical presentations of expander graphs refer to one of several variants. For example, in some sources, expanders are presented as bi- partite graphs, whereas in others they are presented as ordinary graphs (and are in fact very far from being bipartite). We shall follow the latter convention. Furthermore, at times we implicitly consider an augmentation of these graphs where self-loops are added to each vertex. For simplicity, we also allow parallel edges. We often talk of expander graphs while we actually mean an infinite collection of graphs such that each graph in this collection satisfies the same property (which is informally attributed to the collection). -
Connections Between Graph Theory and Cryptography
Connections between graph theory and cryptography Connections between graph theory and cryptography Natalia Tokareva G2C2: Graphs and Groups, Cycles and Coverings September, 24–26, 2014. Novosibirsk, Russia Connections between graph theory and cryptography Introduction to cryptography Introduction to cryptography Connections between graph theory and cryptography Introduction to cryptography Terminology Cryptography is the scientific and practical activity associated with developing of cryptographic security facilities of information and also with argumentation of their cryptographic resistance. Plaintext is a secret message. Often it is a sequence of binary bits. Ciphertext is an encrypted message. Encryption is the process of disguising a message in such a way as to hide its substance (the process of transformation plaintext into ciphertext by virtue of cipher). Cipher is a family of invertible mappings from the set of plaintext sequences to the set of ciphertext sequences. Each mapping depends on special parameter a key. Key is removable part of the cipher. Connections between graph theory and cryptography Introduction to cryptography Terminology Deciphering is the process of turning a ciphertext back into the plaintext that realized with known key. Decryption is the process of receiving the plaintext from ciphertext without knowing the key. Connections between graph theory and cryptography Introduction to cryptography Terminology Cryptography is the scientific and practical activity associated with developing of cryptographic security -
Expander Graphs and Their Applications
Expander Graphs and their Applications Lecture notes for a course by Nati Linial and Avi Wigderson The Hebrew University, Israel. January 1, 2003 Contents 1 The Magical Mystery Tour 7 1.1SomeProblems.............................................. 7 1.1.1 Hardnessresultsforlineartransformation............................ 7 1.1.2 ErrorCorrectingCodes...................................... 8 1.1.3 De-randomizing Algorithms . .................................. 9 1.2MagicalGraphs.............................................. 10 ´Òµ 1.2.1 A Super Concentrator with Ç edges............................. 12 1.2.2 ErrorCorrectingCodes...................................... 12 1.2.3 De-randomizing Random Algorithms . ........................... 13 1.3Conclusions................................................ 14 2 Graph Expansion & Eigenvalues 15 2.1Definitions................................................. 15 2.2ExamplesofExpanderGraphs...................................... 16 2.3TheSpectrumofaGraph......................................... 16 2.4TheExpanderMixingLemmaandApplications............................. 17 2.4.1 DeterministicErrorAmplificationforBPP........................... 18 2.5HowBigCantheSpectralGapbe?.................................... 19 3 Random Walks on Expander Graphs 21 3.1Preliminaries............................................... 21 3.2 A random walk on an expander is rapidly mixing. ........................... 21 3.3 A random walk on an expander yields a sequence of “good samples” . ............... 23 3.3.1 Application: amplifying -
Characterising Bounded Expansion by Neighbourhood Complexity
Characterising Bounded Expansion by Neighbourhood Complexity Felix Reidl Fernando S´anchez Villaamil Konstantinos Stavropoulos RWTH Aachen University freidl,fernando.sanchez,[email protected]. Abstract We show that a graph class G has bounded expansion if and only if it has bounded r-neighbourhood complexity, i.e. for any vertex set X of any subgraph H of G ∈ G, the number of subsets of X which are exact r-neighbourhoods of vertices of H on X is linear in the size of X. This is established by bounding the r-neighbourhood complexity of a graph in terms of both its r-centred colouring number and its weak r-colouring number, which provide known characterisations to the property of bounded expansion. 1 Introduction Graph classes of bounded expansion (and their further generalisation, nowhere dense classes) have been introduced by Neˇsetˇriland Ossona de Mendez [23, 24, 25] as a general model of structurally sparse graph classes. They include and generalise many other natural sparse graph classes, among them all classes of bounded degree, classes of bounded genus, and classes defined by arXiv:1603.09532v2 [cs.DM] 2 Nov 2016 excluded (topological) minors. Nowhere dense classes even include classes that locally exclude a minor, which in turn generalises graphs with locally bounded treewidth. The appeal of this notion and its applications stems from the fact that bounded expansion has turned out to be a very robust property of graph classes with various seemingly unrelated characterisations (see [19, 25]). These include characterisations through the density of shallow minors [23], quasi-wideness [3] low treedepth colourings [23], and generalised colouring numbers [31]. -
Large Minors in Expanders
Large Minors in Expanders Julia Chuzhoy∗ Rachit Nimavaty January 26, 2019 Abstract In this paper we study expander graphs and their minors. Specifically, we attempt to answer the following question: what is the largest function f(n; α; d), such that every n-vertex α-expander with maximum vertex degree at most d contains every graph H with at most f(n; α; d) edges and vertices as a minor? Our main result is that there is some universal constant c, such that f(n; α; d) ≥ n α c c log n · d . This bound achieves a tight dependence on n: it is well known that there are bounded- degree n-vertex expanders, that do not contain any grid with Ω(n= log n) vertices and edges as a minor. The best previous result showed that f(n; α; d) ≥ Ω(n= logκ n), where κ depends on both α and d. Additionally, we provide a randomized algorithm, that, given an n-vertex α-expander with n α c maximum vertex degree at most d, and another graph H containing at most c log n · d vertices and edges, with high probability finds a model of H in G, in time poly(n) · (d/α)O(log(d/α)). We also show a simple randomized algorithm with running time poly(n; d/α), that obtains a similar result with slightly weaker dependence on n but a better dependence on d and α, namely: if G is α3n an n-vertex α-expander with maximum vertex degree at most d, and H contains at most c0d5 log2 n edges and vertices, where c0 is an absolute constant, then our algorithm with high probability finds a model of H in G. -
Lecture Beyond Planar Graphs 1 Overview 2 Preliminaries
Approximation Algorithms Workshop June 17, 2011, Princeton Lecture Beyond Planar Graphs Erik Demaine Scribe: Siamak Tazari 1 Overview In the last lecture we learned about approximation schemes in planar graphs. In this lecture we go beyond planar graphs and consider bounded-genus graphs, and more generally, minor-closed graph classes. In particular, we discuss the framework of bidimensionality that can be used to obtain approximation schemes for many problems in minor-closed graph classes. Afterwards, we consider decomposition techniques that can be applied to other types of problems. The main motivation behind this line of work is that some networks are not planar but conform to some generalization of the concept of planarity, like being embeddable on a surface of bounded genus. Furthermore, by Kuratowski's famous theorem [Kur30], planarity can be characterized be excluding K5 and K3;3 as minors. Hence, it seems natural to study further classes with excluded minors as a generalization of planar graphs. One of the goals of this research is to identify the largest classes of graphs for which we can still ob- tain good and efficient approximations, i.e. to find out how far beyond planarity we can go. Natural candidate classes are graphs of bounded genus, graphs excluding a fixed minor, powers thereof, but also further generalizations, like odd-minor-free graphs, graphs of bounded expansion and nowhere dense classes of graphs. Another objective is to derive general approximation frameworks, two of which we are going to discuss here: bidimensionality and contraction decomposition. The main tools we are going to use come on one hand from graph structure theory and on the other hand from algorithmic results. -
Construction Algorithms for Expander Graphs
Trinity College Trinity College Digital Repository Senior Theses and Projects Student Scholarship Spring 2014 Construction Algorithms for Expander Graphs Vlad S. Burca Trinity College, [email protected] Follow this and additional works at: https://digitalrepository.trincoll.edu/theses Part of the Algebra Commons, Other Applied Mathematics Commons, Other Computer Sciences Commons, and the Theory and Algorithms Commons Recommended Citation Burca, Vlad S., "Construction Algorithms for Expander Graphs". Senior Theses, Trinity College, Hartford, CT 2014. Trinity College Digital Repository, https://digitalrepository.trincoll.edu/theses/393 Construction Algorithms for Expander Graphs VLAD Ș. BURCĂ ’14 Adviser: Takunari Miyazaki Graphs are mathematical objects that are comprised of nodes and edges that connect them. In computer science they are used to model concepts that exhibit network behaviors, such as social networks, communication paths or computer networks. In practice, it is desired that these graphs retain two main properties: sparseness and high connectivity. This is equivalent to having relatively short distances between two nodes but with an overall small number of edges. These graphs are called expander graphs and the main motivation behind studying them is the efficient network structure that they can produce due to their properties. We are specifically interested in the study of k-regular expander graphs, which are expander graphs whose nodes are each connected to exactly k other nodes. The goal of this project is to compare explicit and random methods of generating expander graphs based on the quality of the graphs they produce. This is done by analyzing the graphs’ spectral property, which is an algebraic method of comparing expander graphs. -
On Quasi-Planar Graphs: Clique-Width and Logical Description
On quasi-planar graphs : clique-width and logical description Bruno Courcelle To cite this version: Bruno Courcelle. On quasi-planar graphs : clique-width and logical description. Discrete Applied Mathematics, Elsevier, 2018, 10.1016/j.dam.2018.07.022. hal-01735820v2 HAL Id: hal-01735820 https://hal.archives-ouvertes.fr/hal-01735820v2 Submitted on 3 Mar 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. On quasi-planar graphs : clique-width and logical description. Bruno Courcelle Labri, CNRS and Bordeaux University∗ 33405 Talence, France email: [email protected] June 19, 2018 Abstract Motivated by the construction of FPT graph algorithms parameterized by clique-width or tree-width, we study graph classes for which tree- width and clique-width are linearly related. This is the case for all graph classes of bounded expansion, but in view of concrete applications, we want to have "small" constants in the comparisons between these width parameters. We focus our attention on graphs that can be drawn in the plane with limited edge crossings, for an example, at most p crossings for each edge. These graphs are called p-planar. We consider a more general situation where the graph of edge crossings must belong to a fixed class of graphs . -
Lecture 7: Expander Graphs in Computer Science 1 Spectra of Graphs
Great Ideas in Theoretical Computer Science Summer 2013 Lecture 7: Expander Graphs in Computer Science Lecturer: Kurt Mehlhorn & He Sun Over the past decades expanders have become one basic tool in various areas of computer science, including algorithm design, complexity theory, coding theory, and etc. Informally, expanders are regular graphs with low degree and high connectivity. We can use different ways to define expander graphs. 1. Combinatorically, expanders are highly connected graphs, and to disconnect a large part of the graph, one has to sever many edges. 2. Geometrically, every vertex set has a relatively large boundary. 3. From the Probabilistic view, expanders are graphs whose behavior is \like" ran- dom graphs. 4. Algebraically, expanders are the real-symmetric matrix whose first positive eigen- value of the Laplace operator is bounded away from zero. 1 Spectra of Graphs Definition 1. Let G = (V; E) be an undirected graph with vertex set [n] , f1; : : : ; ng. The adjacency matrix of G is an n by n matrix A given by ( 1 if i and j are adjacent A = i;j 0 otherwise If G is a multi-graph, then Ai;j is the number of edges between vertex i and vertex j. By definition, the adjacency matrix of graphs has the following properties: (1) The sum of elements in every row/column equals the degree of the corresponding vertex. (2) If G is undirected, then A is symmetric. Example. The adjacency matrix of a triangle is 0 0 1 1 1 @ 1 0 1 A 1 1 0 Given a matrix A, a vector x 6= 0 is defined to be an eigenvector of A if and only if there is a λ 2 C such that Ax = λx. -
Characterisations and Examples of Graph Classes with Bounded Expansion Jaroslav Nešetřil A, Patrice Ossona De Mendez B, David R
European Journal of Combinatorics ( ) – Contents lists available at SciVerse ScienceDirect European Journal of Combinatorics journal homepage: www.elsevier.com/locate/ejc Characterisations and examples of graph classes with bounded expansion Jaroslav Nešetřil a, Patrice Ossona de Mendez b, David R. Wood c a Department of Applied Mathematics, and Institute for Theoretical Computer Science, Charles University, Prague, Czech Republic b Centre d'Analyse et de Mathématique Sociales, Centre National de la Recherche Scientifique, and École des Hautes Études en Sciences Sociales, Paris, France c Department of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia article info a b s t r a c t Article history: Classes with bounded expansion, which generalise classes that Available online xxxx exclude a topological minor, have recently been introduced by Nešetřil and Ossona de Mendez. These classes are defined by the fact that the maximum average degree of a shallow minor of a graph in the class is bounded by a function of the depth of the shallow minor. Several linear-time algorithms are known for bounded expansion classes (such as subgraph isomorphism testing), and they allow restricted homomorphism dualities, amongst other desirable properties. In this paper, we establish two new characterisations of bounded expansion classes, one in terms of so-called topological parameters and the other in terms of controlling dense parts. The latter characterisation is then used to show that the notion of bounded expansion is compatible with the Erdös–Rényi model of random graphs with constant average degree. In particular, we prove that for every fixed d > 0, there exists a class with bounded expansion, such that a random graph of order n and edge probability d=n asymptotically almost surely belongs to the class.