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Modern Discrete Probability I
Preliminaries Some fundamental models A few more useful facts about... Modern Discrete Probability I - Introduction Stochastic processes on graphs: models and questions Sebastien´ Roch UW–Madison Mathematics September 6, 2017 Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about... 1 Preliminaries Review of graph theory Review of Markov chain theory 2 Some fundamental models Random walks on graphs Percolation Some random graph models Markov random fields Interacting particles on finite graphs 3 A few more useful facts about... ...graphs ...Markov chains ...other things Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about... Graphs Definition (Undirected graph) An undirected graph (or graph for short) is a pair G = (V ; E) where V is the set of vertices (or nodes, sites) and E ⊆ ffu; vg : u; v 2 V g; is the set of edges (or bonds). The V is either finite or countably infinite. Edges of the form fug are called loops. We do not allow E to be a multiset. We occasionally write V (G) and E(G) for the vertices and edges of G. Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about... An example: the Petersen graph Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about.. -
Arxiv:1902.03126V3 [Math.CO] 23 Jan 2020
The independence number of HH-homogeneous graphs and a classification of MB-homogeneous graphs Andr´esArandaa, David Hartmanb,c aInstitut f¨urAlgebra, Technische Universit¨atDresden, Zellescher Weg 12-14, Dresden. bComputer Science Institute of Charles University, Charles University, Malostransk´en´am. 25, Prague 1 cInstitute of Computer Science of the Czech Academy of Sciences, Pod Vod´arenskou vˇeˇz´ı 271/2, Prague 8 Abstract We show that the independence number of a countably infinite connected HH- homogeneous graph that does not contain the Rado graph as a spanning sub- graph is finite and present a classification of MB-homogeneous graphs up to bimorphism-equivalence as a consequence. Keywords: homomorphism-homogeneity, morphism-extension classes, HH-homogeneity, MB-homogeneity 2010 MSC: 03C15, 05C60, 05C63, 05C69, 05C75 1. Introduction The symmetry of graphs, or more generally relational structures, is usually measured by such numbers as the degree of transitivity or homogeneity of the natural action of their automorphism group. One of the strongest notions of symmetry is ultrahomogeneity, defined as the property that any isomorphism between two finite induced subgraphs can be extended to an automorphism. This notion was generalized by Cameron and Neˇsetˇril,in [1], to homomorphism- homogeneity, requiring that any local homomorphism (that is, a homomorphism between finite induced substructures) extends to an endomorphism of the am- bient structure. By specifying the type of local homomorphism and endomor- phism, several new morphism-extension classes were introduced by Lockett and arXiv:1902.03126v3 [math.CO] 23 Jan 2020 Truss (see [2]), each denoted by a pair of characters as XY and defined by the condition that any local X-morphism extends to a global Y -morphism. -
Universality for and in Induced-Hereditary Graph Properties
Discussiones Mathematicae Graph Theory 33 (2013) 33–47 doi:10.7151/dmgt.1671 Dedicated to Mieczys law Borowiecki on his 70th birthday UNIVERSALITY FOR AND IN INDUCED-HEREDITARY GRAPH PROPERTIES Izak Broere Department of Mathematics and Applied Mathematics University of Pretoria e-mail: [email protected] and Johannes Heidema Department of Mathematical Sciences University of South Africa e-mail: [email protected] Abstract The well-known Rado graph R is universal in the set of all countable graphs , since every countable graph is an induced subgraph of R. We I study universality in and, using R, show the existence of 2ℵ0 pairwise non-isomorphic graphsI which are universal in and denumerably many other universal graphs in with prescribed attributes.I Then we contrast universality for and universalityI in induced-hereditary properties of graphs and show that the overwhelming majority of induced-hereditary properties contain no universal graphs. This is made precise by showing that there are 2(2ℵ0 ) properties in the lattice K of induced-hereditary properties of which ≤ only at most 2ℵ0 contain universal graphs. In a final section we discuss the outlook on future work; in particular the question of characterizing those induced-hereditary properties for which there is a universal graph in the property. Keywords: countable graph, universal graph, induced-hereditary property. 2010 Mathematics Subject Classification: 05C63. 34 I.Broere and J. Heidema 1. Introduction and Motivation In this article a graph shall (with one illustrative exception) be simple, undirected, unlabelled, with a countable (i.e., finite or denumerably infinite) vertex set. For graph theoretical notions undefined here, we generally follow [14]. -
Generating Infinite Random Graphs
GENERATING INFINITE RANDOM GRAPHS CSABA BIRO´ AND UDAYAN B. DARJI Abstract. We define a growing model of random graphs. Given a sequence ∞ of nonnegative integers {dn}n=0 with the property that di ≤ i, we construct a random graph on countably infinitely many vertices v0, v1 ... by the following process: vertex vi is connected to a subset of {v0,...,vi−1} of cardinality di chosen uniformly at random. We study the resulting probability space. In particular, we give a new characterization of random graph and we also give probabilistic methods for constructing infinite random trees. 1. Introduction Consider the vertex set N. Let 0 <p< 1 be fixed. For each pair of distinct integers n,m N, put an edge between n and m with probability p. Let G be the resulting graph∈ on N. A classical 1963 Erd˝os–R´enyi theorem [10] states that with probability one, any two such graphs are isomorphic, i.e., there is essentially one random graph on N. In 1964 Rado [15] gave an explicit construction of a graph R which is universal for the collection of all countable graphs. More precisely, he showed that if G and H are any countable graphs and φ : G H a graph homomorphism, then there → are embeddings eG : G R, eH : H R and a graph homomorphism ψ : R R −1 → → → such that eH ψ eG = φ, i.e., R contains a copy of every countable graph and every graph homomorphism◦ ◦ between countable graphs can be lifted to a graph homomorphism of R. The constructions of Erd˝os–R´enyi and Rado seem very different but they result in the same graph. -
Markov Chain Monte Carlo Estimation of Exponential Random Graph Models
Markov Chain Monte Carlo Estimation of Exponential Random Graph Models Tom A.B. Snijders ICS, Department of Statistics and Measurement Theory University of Groningen ∗ April 19, 2002 ∗Author's address: Tom A.B. Snijders, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands, email <[email protected]>. I am grateful to Paul Snijders for programming the JAVA applet used in this article. In the revision of this article, I profited from discussions with Pip Pattison and Garry Robins, and from comments made by a referee. This paper is formatted in landscape to improve on-screen readability. It is read best by opening Acrobat Reader in a full screen window. Note that in Acrobat Reader, the entire screen can be used for viewing by pressing Ctrl-L; the usual screen is returned when pressing Esc; it is possible to zoom in or zoom out by pressing Ctrl-- or Ctrl-=, respectively. The sign in the upper right corners links to the page viewed previously. ( Tom A.B. Snijders 2 MCMC estimation for exponential random graphs ( Abstract bility to move from one region to another. In such situations, convergence to the target distribution is extremely slow. To This paper is about estimating the parameters of the exponential be useful, MCMC algorithms must be able to make transitions random graph model, also known as the p∗ model, using frequen- from a given graph to a very different graph. It is proposed to tist Markov chain Monte Carlo (MCMC) methods. The exponen- include transitions to the graph complement as updating steps tial random graph model is simulated using Gibbs or Metropolis- to improve the speed of convergence to the target distribution. -
Exploring Healing Strategies for Random Boolean Networks
Exploring Healing Strategies for Random Boolean Networks Christian Darabos 1, Alex Healing 2, Tim Johann 3, Amitabh Trehan 4, and Am´elieV´eron 5 1 Information Systems Department, University of Lausanne, Switzerland 2 Pervasive ICT Research Centre, British Telecommunications, UK 3 EML Research gGmbH, Heidelberg, Germany 4 Department of Computer Science, University of New Mexico, Albuquerque, USA 5 Division of Bioinformatics, Institute for Evolution and Biodiversity, The Westphalian Wilhelms University of Muenster, Germany. [email protected], [email protected], [email protected], [email protected], [email protected] Abstract. Real-world systems are often exposed to failures where those studied theoretically are not: neuron cells in the brain can die or fail, re- sources in a peer-to-peer network can break down or become corrupt, species can disappear from and environment, and so forth. In all cases, for the system to keep running as it did before the failure occurred, or to survive at all, some kind of healing strategy may need to be applied. As an example of such a system subjected to failure and subsequently heal, we study Random Boolean Networks. Deletion of a node in the network was considered a failure if it affected the functional output and we in- vestigated healing strategies that would allow the system to heal either fully or partially. More precisely, our main strategy involves allowing the nodes directly affected by the node deletion (failure) to iteratively rewire in order to achieve healing. We found that such a simple method was ef- fective when dealing with small networks and single-point failures. -
Percolation Threshold Results on Erdos-Rényi Graphs
arXiv: arXiv:0000.0000 Percolation Threshold Results on Erd}os-R´enyi Graphs: an Empirical Process Approach Michael J. Kane Abstract: Keywords and phrases: threshold, directed percolation, stochastic ap- proximation, empirical processes. 1. Introduction Random graphs and discrete random processes provide a general approach to discovering properties and characteristics of random graphs and randomized al- gorithms. The approach generally works by defining an algorithm on a random graph or a randomized algorithm. Then, expected changes for each step of the process are used to propose a limiting differential equation and a large deviation theorem is used to show that the process and the differential equation are close in some sense. In this way a connection is established between the resulting process's stochastic behavior and the dynamics of a deterministic, asymptotic approximation using a differential equation. This approach is generally referred to as stochastic approximation and provides a powerful tool for understanding the asymptotic behavior of a large class of processes defined on random graphs. However, little work has been done in the area of random graph research to investigate the weak limit behavior of these processes before the asymptotic be- havior overwhelms the random component of the process. This context is par- ticularly relevant to researchers studying news propagation in social networks, sensor networks, and epidemiological outbreaks. In each of these applications, investigators may deal graphs containing tens to hundreds of vertices and be interested not only in expected behavior over time but also error estimates. This paper investigates the connectivity of graphs, with emphasis on Erd}os- R´enyi graphs, near the percolation threshold when the number of vertices is not asymptotically large. -
Probability on Graphs Random Processes on Graphs and Lattices
Probability on Graphs Random Processes on Graphs and Lattices GEOFFREY GRIMMETT Statistical Laboratory University of Cambridge c G. R. Grimmett 1/4/10, 17/11/10, 5/7/12 Geoffrey Grimmett Statistical Laboratory Centre for Mathematical Sciences University of Cambridge Wilberforce Road Cambridge CB3 0WB United Kingdom 2000 MSC: (Primary) 60K35, 82B20, (Secondary) 05C80, 82B43, 82C22 With 44 Figures c G. R. Grimmett 1/4/10, 17/11/10, 5/7/12 Contents Preface ix 1 Random walks on graphs 1 1.1 RandomwalksandreversibleMarkovchains 1 1.2 Electrical networks 3 1.3 Flowsandenergy 8 1.4 Recurrenceandresistance 11 1.5 Polya's theorem 14 1.6 Graphtheory 16 1.7 Exercises 18 2 Uniform spanning tree 21 2.1 De®nition 21 2.2 Wilson's algorithm 23 2.3 Weak limits on lattices 28 2.4 Uniform forest 31 2.5 Schramm±LownerevolutionsÈ 32 2.6 Exercises 37 3 Percolation and self-avoiding walk 39 3.1 Percolationandphasetransition 39 3.2 Self-avoiding walks 42 3.3 Coupledpercolation 45 3.4 Orientedpercolation 45 3.5 Exercises 48 4 Association and in¯uence 50 4.1 Holley inequality 50 4.2 FKGinequality 53 4.3 BK inequality 54 4.4 Hoeffdinginequality 56 c G. R. Grimmett 1/4/10, 17/11/10, 5/7/12 vi Contents 4.5 In¯uenceforproductmeasures 58 4.6 Proofsofin¯uencetheorems 63 4.7 Russo'sformulaandsharpthresholds 75 4.8 Exercises 78 5 Further percolation 81 5.1 Subcritical phase 81 5.2 Supercritical phase 86 5.3 Uniquenessofthein®nitecluster 92 5.4 Phase transition 95 5.5 Openpathsinannuli 99 5.6 The critical probability in two dimensions 103 5.7 Cardy's formula 110 5.8 The -
3 a Few More Good Inequalities, Martingale Variety 1 3.1 From
3 A few more good inequalities, martingale variety1 3.1 From independence to martingales...............1 3.2 Hoeffding’s inequality for martingales.............4 3.3 Bennett's inequality for martingales..............8 3.4 *Concentration of random polynomials............. 10 3.5 *Proof of the Kim-Vu inequality................ 14 3.6 Problems............................. 18 3.7 Notes............................... 18 Printed: 8 October 2015 version: 8Oct2015 Mini-empirical printed: 8 October 2015 c David Pollard x3.1 From independence to martingales 1 Chapter 3 A few more good inequalities, martingale variety Section 3.1 introduces the method for bounding tail probabilities using mo- ment generating functions. Section 3.2 discusses the Hoeffding inequality, both for sums of independent bounded random variables and for martingales with bounded increments. Section 3.3 discusses the Bennett inequality, both for sums of indepen- dent random variables that are bounded above by a constant and their martingale analogs. *Section3.4 presents an extended application of a martingale version of the Bennett inequality to derive a version of the Kim-Vu inequality for poly- nomials in independent, bounded random variables. 3.1 From independence to martingales BasicMG::S:intro Throughout this Chapter f(Si; Fi): i = 1; : : : ; ng is a martingale on some probability space (Ω; F; P). That is, we have a sub-sigma fields F0 ⊆ F1 ⊆ · · · ⊆ Fn ⊆ F and integrable, Fi-measurable random variables Si for which PFi−1 Si = Si−1 almost surely. Equivalently, the martingale differ- ences ξi := Si − Si−1 are integrable, Fi-measurable, and PFi−1 ξi = 0 almost surely, for i = 1; : : : ; n. In that case Si = S0 + ξ1 + ··· + ξi = Si−1 + ξi. -
Arxiv:2107.01710V2 [Math.CO] 9 Jul 2021
Infinite Ramsey-minimal graphs for star forests Fawwaz Fakhrurrozi Hadiputra, Valentino Vito Department of Mathematics, Universitas Indonesia, Depok 16424, Indonesia Abstract For graphs F , G, and H, we write F → (G, H) if every red-blue coloring of the edges of F produces a red copy of G or a blue copy of H. The graph F is said to be (G, H)-minimal if it is subgraph-minimal with respect to this property. The characterization problem for Ramsey-minimal graphs is classically done for finite graphs. In 2021, Barrett and the second author generalized this problem to infinite graphs. They asked which pairs (G, H) admit a Ramsey-minimal graph and which ones do not. We show that any pair of star forests such that at least one of them involves an infinite-star component admits no Ramsey- minimal graph. Also, we construct a Ramsey-minimal graph for a finite star forest versus a subdivision graph. This paper builds upon the results of Burr et al. in 1981 on Ramsey-minimal graphs for finite star forests. Key words: Ramsey-minimal graph, infinite graph, graph embedding, star forest, subdivision graph 2020 MSC: 05C55, 05C63, 05C35, 05C60, 05D10 1. Introduction All our graphs are simple and undirected. We start by stating basic def- initions. For graphs F , G and H, we write F → (G, H) if every red-blue coloring of the edges of F produces a red copy of G or a blue copy of H. A red-blue coloring of F is (G, H)-good if it produces neither a red G nor a blue H. -
Random Boolean Networks As a Toy Model for the Brain
UNIVERSITY OF GENEVA SCIENCE FACULTY VRIJE UNIVERSITEIT OF AMSTERDAM PHYSICS SECTION Random Boolean Networks as a toy model for the brain MASTER THESIS presented at the science faculty of the University of Geneva for obtaining the Master in Theoretical Physics by Chlo´eB´eguin Supervisor (VU): Pr. Greg J Stephens Co-Supervisor (UNIGE): Pr. J´er^ome Kasparian July 2017 Contents Introduction1 1 Biology, physics and the brain4 1.1 Biological description of the brain................4 1.2 Criticality in the brain......................8 1.2.1 Physics reminder..................... 10 1.2.2 Experimental evidences.................. 15 2 Models of neural networks 20 2.1 Classes of models......................... 21 2.1.1 Spiking models...................... 21 2.1.2 Rate-based models.................... 23 2.1.3 Attractor networks.................... 24 2.1.4 Links between the classes of models........... 25 2.2 Random Boolean Networks.................... 28 2.2.1 General definition..................... 28 2.2.2 Kauffman network.................... 30 2.2.3 Hopfield network..................... 31 2.2.4 Towards a RBN for the brain.............. 32 2.2.5 The model......................... 33 3 Characterisation of RBNs 34 3.1 Attractors............................. 34 3.2 Damage spreading........................ 36 3.3 Canonical specific heat...................... 37 4 Results 40 4.1 One population with Gaussian weights............. 40 4.2 Dale's principle and balance of inhibition - excitation..... 46 4.3 Lognormal distribution of the weights.............. 51 4.4 Discussion............................. 55 i 5 Conclusion 58 Bibliography 60 Acknowledgements 66 A Python Code 67 A.1 Dynamics............................. 67 A.2 Attractor search.......................... 69 A.3 Hamming Distance........................ 73 A.4 Canonical specific heat..................... -
An Introduction to Exponential Random Graph (P*) Models for Social Networks
Social Networks 29 (2007) 173–191 An introduction to exponential random graph (p*) models for social networks Garry Robins ∗, Pip Pattison, Yuval Kalish, Dean Lusher Department of Psychology, School of Behavioural Science, University of Melbourne, Vic. 3010, Australia Abstract This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov random graph models of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponential random graph models. Sociological Methodology, in press] offer substantial improvement. © 2006 Elsevier B.V. All rights reserved. Keywords: Exponential random graph models; Statistical models for social networks; p* models In recent years, there has been growing interest in exponential random graph models for social networks, commonly called the p* class of models (Frank and Strauss, 1986; Pattison and Wasserman, 1999; Robins et al., 1999; Wasserman and Pattison, 1996).