We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Analytic Combinatorics is a self-contained treatment of the mathematics underlying the analysis of discrete structures, which has emerged over the past several decades as an essential tool in the understanding of properties of computer programs and scientific models with applications in physics, biology and chemistry. Thorough treatment of a large number of classical applications is an essential aspect of the presentation. Written by the leaders in the field of analytic combinatorics, this text is certain to become the definitive reference on the topic. The text is complemented with exercises, examples, appendices and notes to aid understanding therefore, it can be used as the basis for an advanced undergraduate or a graduate course on the subject, or for self-study.

Analytic Combinatorics

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

(2002). Modelling Extremal Events for Insurance and Finance. Journal of the American Statistical Association: Vol. 97, No. 457, pp. 360-360.

Modelling Extremal Events for Insurance and Finance

A wealth of protein and DNA sequence data is being generated by genome projects and other sequencing efforts. A crucial barrier to deciphering these sequences and understanding the relations among them is the difficulty of detecting subtle local residue patterns common to multiple sequences. Such patterns frequently reflect similar molecular structures and biological properties. A mathematical definition of this "local multiple alignment" problem suitable for full computer automation has been used to develop a new and sensitive algorithm, based on the statistical method of iterative sampling. This algorithm finds an optimized local alignment model for N sequences in N-linear time, requiring only seconds on current workstations, and allows the simultaneous detection and optimization of multiple patterns and pattern repeats. The method is illustrated as applied to helix-turn-helix proteins, lipocalins, and prenyltransferases.

/pdf/detecting-subtle-sequence-signals-a-gibbs-sampling-strategy-56bu9neqln.pdf

Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment

This is the final version of the article. It first appeared from the Institute of Mathematical Statistics via http://dx.doi.org/10.1214/15-STS537

/pdf/exploiting-the-feller-coupling-for-the-ewens-sampling-64q5le9jk6.pdf

Exploiting the Feller Coupling for the Ewens Sampling Formula

/pdf/review-d-aldous-probability-approximations-via-the-poisson-57xao2kzmd.pdf

Review: D. Aldous, Probability Approximations via the Poisson Clumping Heuristic; A. D. Barbour, L. Holst, S. Janson, Poisson Approximation

The distribution of clusters in random graphs

We study the random variable B(c, n), which counts the number of balls that must be thrown into n equally-sized bins in order to obtain c collisions. The asymptotic expected value of B(1, n) is the well-known appearing in the solution to the birthday problem; the limit distribution and asymptotic moments of B(1, n) are also well known. We calculate the distribution and moments of B(c, n) asymptotically as n goes to ∞ and c = O(n). We have two main tools: an embedding of the collision process — realizing the process as a deterministic function of the standard Poisson process — and a central limit result by Renyi. © 2015 Wiley Periodicals, Inc. Random Struct. Alg., 2015

/pdf/asymptotic-distribution-for-the-birthday-problem-with-1uukad9iyn.pdf

Asymptotic distribution for the birthday problem with multiple coincidences, via an embedding of the collision process†

We study the random variable B(c,n), which counts the number of balls that must be thrown into n equally-sized bins in order to obtain c collisions. The asymptotic expected value of B(1,n) is the well-known $\sqrt{n\pi/2}$ appearing in the solution to the birthday problem; the limit distribution and asymptotic moments of B(1,n) are also well known. We calculate the distribution and moments of B(c,n) asymptotically as n goes to infinity and c = O(n). 
Our main tools are an embedding of the collision process, realizing the process as a deterministic function of the standard Poisson process, and a central limit result by Renyi.

Richard Arratia

Papers

Exploiting the Feller Coupling for the Ewens Sampling Formula

Review: D. Aldous, Probability Approximations via the Poisson Clumping Heuristic; A. D. Barbour, L. Holst, S. Janson, Poisson Approximation

The distribution of clusters in random graphs

Asymptotic distribution for the birthday problem with multiple coincidences, via an embedding of the collision process†

Asymptotic distribution for the birthday problem with multiple coincidences, via an embedding of the collision process