Random Graph Dynamics
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Poisson Representations of Branching Markov and Measure-Valued
The Annals of Probability 2011, Vol. 39, No. 3, 939–984 DOI: 10.1214/10-AOP574 c Institute of Mathematical Statistics, 2011 POISSON REPRESENTATIONS OF BRANCHING MARKOV AND MEASURE-VALUED BRANCHING PROCESSES By Thomas G. Kurtz1 and Eliane R. Rodrigues2 University of Wisconsin, Madison and UNAM Representations of branching Markov processes and their measure- valued limits in terms of countable systems of particles are con- structed for models with spatially varying birth and death rates. Each particle has a location and a “level,” but unlike earlier con- structions, the levels change with time. In fact, death of a particle occurs only when the level of the particle crosses a specified level r, or for the limiting models, hits infinity. For branching Markov pro- cesses, at each time t, conditioned on the state of the process, the levels are independent and uniformly distributed on [0,r]. For the limiting measure-valued process, at each time t, the joint distribu- tion of locations and levels is conditionally Poisson distributed with mean measure K(t) × Λ, where Λ denotes Lebesgue measure, and K is the desired measure-valued process. The representation simplifies or gives alternative proofs for a vari- ety of calculations and results including conditioning on extinction or nonextinction, Harris’s convergence theorem for supercritical branch- ing processes, and diffusion approximations for processes in random environments. 1. Introduction. Measure-valued processes arise naturally as infinite sys- tem limits of empirical measures of finite particle systems. A number of ap- proaches have been developed which preserve distinct particles in the limit and which give a representation of the measure-valued process as a transfor- mation of the limiting infinite particle system. -
Abstract We Survey the Published Work of Harry Kesten in Probability Theory, with Emphasis on His Contributions to Random Walks
Abstract We survey the published work of Harry Kesten in probability theory, with emphasis on his contributions to random walks, branching processes, perco- lation, and related topics. Keywords Probability, random walk, branching process, random matrix, diffusion limited aggregation, percolation. Mathematics Subject Classification (2010) 60-03, 60G50, 60J80, 60B20, 60K35, 82B20. Noname manuscript No. 2(will be inserted by the editor) Geoffrey R. Grimmett Harry Kesten's work in probability theory Geoffrey R. Grimmett In memory of Harry Kesten, inspiring colleague, valued friend April 8, 2020 1 Overview Harry Kesten was a prominent mathematician and personality in a golden period of probability theory from 1956 to 2018. At the time of Harry's move from the Netherlands to the USA in 1956, as a graduate student aged 24, much of the foundational infrastructure of probability was in place. The central characters of probability had long been identified (including random walk, Brownian motion, the branching process, and the Poisson process), and connections had been made and developed between `pure theory' and cognate areas ranging from physics to finance. In the half-century or so since 1956, a coordinated and refined theory has been developed, and probability has been recognised as a crossroads discipline in mathematical science. Few mathematicians have contributed as much during this period as Harry Kesten. Following a turbulent childhood (see [59]), Harry studied mathematics with David van Dantzig and Jan Hemelrijk in Amsterdam, where in 1955 he attended a lecture by Mark Kac entitled \Some probabilistic aspects of potential theory". This encounter appears to have had a decisive effect, in that Harry moved in 1956 to Cornell University to work with Kac. -
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.. -
I. Overview of Activities, April, 2005-March, 2006 …
MATHEMATICAL SCIENCES RESEARCH INSTITUTE ANNUAL REPORT FOR 2005-2006 I. Overview of Activities, April, 2005-March, 2006 …......……………………. 2 Innovations ………………………………………………………..... 2 Scientific Highlights …..…………………………………………… 4 MSRI Experiences ….……………………………………………… 6 II. Programs …………………………………………………………………….. 13 III. Workshops ……………………………………………………………………. 17 IV. Postdoctoral Fellows …………………………………………………………. 19 Papers by Postdoctoral Fellows …………………………………… 21 V. Mathematics Education and Awareness …...………………………………. 23 VI. Industrial Participation ...…………………………………………………… 26 VII. Future Programs …………………………………………………………….. 28 VIII. Collaborations ………………………………………………………………… 30 IX. Papers Reported by Members ………………………………………………. 35 X. Appendix - Final Reports ……………………………………………………. 45 Programs Workshops Summer Graduate Workshops MSRI Network Conferences MATHEMATICAL SCIENCES RESEARCH INSTITUTE ANNUAL REPORT FOR 2005-2006 I. Overview of Activities, April, 2005-March, 2006 This annual report covers MSRI projects and activities that have been concluded since the submission of the last report in May, 2005. This includes the Spring, 2005 semester programs, the 2005 summer graduate workshops, the Fall, 2005 programs and the January and February workshops of Spring, 2006. This report does not contain fiscal or demographic data. Those data will be submitted in the Fall, 2006 final report covering the completed fiscal 2006 year, based on audited financial reports. This report begins with a discussion of MSRI innovations undertaken this year, followed by highlights -
Coalescence in Bellman-Harris and Multi-Type Branching Processes Jyy-I Joy Hong Iowa State University
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2011 Coalescence in Bellman-Harris and multi-type branching processes Jyy-i Joy Hong Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Mathematics Commons Recommended Citation Hong, Jyy-i Joy, "Coalescence in Bellman-Harris and multi-type branching processes" (2011). Graduate Theses and Dissertations. 10103. https://lib.dr.iastate.edu/etd/10103 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Coalescence in Bellman-Harris and multi-type branching processes by Jyy-I Hong A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Mathematics Program of Study Committee: Krishna B. Athreya, Major Professor Clifford Bergman Dan Nordman Ananda Weerasinghe Paul E. Sacks Iowa State University Ames, Iowa 2011 Copyright c Jyy-I Hong, 2011. All rights reserved. ii DEDICATION I would like to dedicate this thesis to my parents Wan-Fu Hong and Wen-Hsiang Tseng for their un- conditional love and support. Without them, the completion of this work would not have been possible. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS . vii ABSTRACT . viii CHAPTER 1. PRELIMINARIES . 1 1.1 Introduction . 1 1.2 Discrete-time Single-type Galton-Watson Branching Processes . -
Lecture 16: March 12 Instructor: Alistair Sinclair
CS271 Randomness & Computation Spring 2020 Lecture 16: March 12 Instructor: Alistair Sinclair Disclaimer: These notes have not been subjected to the usual scrutiny accorded to formal publications. They may be distributed outside this class only with the permission of the Instructor. 16.1 The Giant Component in Gn,p In an earlier lecture we briefly mentioned the threshold for the existence of a “giant” component in a random graph, i.e., a connected component containing a constant fraction of the vertices. We now derive this threshold rigorously, using both Chernoff bounds and the useful machinery of branching processes. We work c with our usual model of random graphs, Gn,p, and look specifically at the range p = n , for some constant c. Our goal will be to prove: c Theorem 16.1 For G ∈ Gn,p with p = n for constant c, we have: 1. For c < 1, then a.a.s. the largest connected component of G is of size O(log n). 2. For c > 1, then a.a.s. there exists a single largest component of G of size βn(1 + o(1)), where β is the unique solution in (0, 1) to β + e−βc = 1. Moreover, the next largest component in G has size O(log n). Here, and throughout this lecture, we use the phrase “a.a.s.” (asymptotically almost surely) to denote an event that holds with probability tending to 1 as n → ∞. This behavior is shown pictorially in Figure 16.1. For c < 1, G consists of a collection of small components of size at most O(log n) (which are all “tree-like”), while for c > 1 a single “giant” component emerges that contains a constant fraction of the vertices, with the remaining vertices all belonging to tree-like components of size O(log n). -
Processes on Complex Networks. Percolation
Chapter 5 Processes on complex networks. Percolation 77 Up till now we discussed the structure of the complex networks. The actual reason to study this structure is to understand how this structure influences the behavior of random processes on networks. I will talk about two such processes. The first one is the percolation process. The second one is the spread of epidemics. There are a lot of open problems in this area, the main of which can be innocently formulated as: How the network topology influences the dynamics of random processes on this network. We are still quite far from a definite answer to this question. 5.1 Percolation 5.1.1 Introduction to percolation Percolation is one of the simplest processes that exhibit the critical phenomena or phase transition. This means that there is a parameter in the system, whose small change yields a large change in the system behavior. To define the percolation process, consider a graph, that has a large connected component. In the classical settings, percolation was actually studied on infinite graphs, whose vertices constitute the set Zd, and edges connect each vertex with nearest neighbors, but we consider general random graphs. We have parameter ϕ, which is the probability that any edge present in the underlying graph is open or closed (an event with probability 1 − ϕ) independently of the other edges. Actually, if we talk about edges being open or closed, this means that we discuss bond percolation. It is also possible to talk about the vertices being open or closed, and this is called site percolation. -
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. -
Multiplex Conductance and Gossip Based Information Spreading in Multiplex Networks
1 Multiplex Conductance and Gossip Based Information Spreading in Multiplex Networks Yufan Huang, Student Member, IEEE, and Huaiyu Dai, Fellow, IEEE Abstract—In this network era, not only people are connected, different networks are also coupled through various interconnections. This kind of network of networks, or multilayer networks, has attracted research interest recently, and many beneficial features have been discovered. However, quantitative study of information spreading in such networks is essentially lacking. Despite some existing results in single networks, the layer heterogeneity and complicated interconnections among the layers make the study of information spreading in this type of networks challenging. In this work, we study the information spreading time in multiplex networks, adopting the gossip (random-walk) based information spreading model. A new metric called multiplex conductance is defined based on the multiplex network structure and used to quantify the information spreading time in a general multiplex network in the idealized setting. Multiplex conductance is then evaluated for some interesting multiplex networks to facilitate understanding in this new area. Finally, the tradeoff between the information spreading efficiency improvement and the layer cost is examined to explain the user’s social behavior and motivate effective multiplex network designs. Index Terms—Information spreading, multiplex networks, gossip algorithm, multiplex conductance F 1 INTRODUCTION N the election year, one of the most important tasks Arguably, this somewhat simplified version of multilayer I for presidential candidates is to disseminate their words networks already captures many interesting multi-scale and and opinions to voters in a fast and efficient manner. multi-component features, and serves as a good starting The underlying research problem on information spreading point for our intended study. -
Chapter 21 Epidemics
From the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World. By David Easley and Jon Kleinberg. Cambridge University Press, 2010. Complete preprint on-line at http://www.cs.cornell.edu/home/kleinber/networks-book/ Chapter 21 Epidemics The study of epidemic disease has always been a topic where biological issues mix with social ones. When we talk about epidemic disease, we will be thinking of contagious diseases caused by biological pathogens — things like influenza, measles, and sexually transmitted diseases, which spread from person to person. Epidemics can pass explosively through a population, or they can persist over long time periods at low levels; they can experience sudden flare-ups or even wave-like cyclic patterns of increasing and decreasing prevalence. In extreme cases, a single disease outbreak can have a significant effect on a whole civilization, as with the epidemics started by the arrival of Europeans in the Americas [130], or the outbreak of bubonic plague that killed 20% of the population of Europe over a seven-year period in the 1300s [293]. 21.1 Diseases and the Networks that Transmit Them The patterns by which epidemics spread through groups of people is determined not just by the properties of the pathogen carrying it — including its contagiousness, the length of its infectious period, and its severity — but also by network structures within the population it is affecting. The social network within a population — recording who knows whom — determines a lot about how the disease is likely to spread from one person to another. But more generally, the opportunities for a disease to spread are given by a contact network: there is a node for each person, and an edge if two people come into contact with each other in a way that makes it possible for the disease to spread from one to the other. -
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.