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

Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field. He gives clear, lucid explanations of key results and ideas, with intuitive proofs, and provides critical examples and numerous illustrations to help elucidate the algorithms. Many of the results presented have been simplified and new insights provided. Of interest to theoretical computer scientists, operations researchers, and discrete mathematicians.

Approximation Algorithms

Complex networks arise in a wide range of biological and sociotechnical systems. Epidemic spreading is central to our understanding of dynamical processes in complex networks, and is of interest to physicists, mathematicians, epidemiologists, and computer and social scientists. This review presents the main results and paradigmatic models in infectious disease modeling and generalized social contagion processes.

Epidemic processes in complex networks

https://deim.urv.cat/~alexandre.arenas/publicacions/pdf/review.pdf

Synchronization in complex networks

This is a tutorial on some basic non-asymptotic methods and concepts in random matrix theory. The reader will learn several tools for the analysis of the extreme singular values of random matrices with independent rows or columns. Many of these methods sprung off from the development of geometric functional analysis since the 1970's. They have applications in several fields, most notably in theoretical computer science, statistics and signal processing. A few basic applications are covered in this text, particularly for the problem of estimating covariance matrices in statistics and for validating probabilistic constructions of measurement matrices in compressed sensing. These notes are written particularly for graduate students and beginning researchers in different areas, including functional analysts, probabilists, theoretical statisticians, electrical engineers, and theoretical computer scientists.

Introduction to the non-asymptotic analysis of random matrices.

The cover time C of a graph G is the expected time for a random walk starting from the worst vertex to cover all vertices in G. Similarly, the blanket time B is the expected time to visit all vertices within a constant factor of number of times suggested by the stationary distribution. (Our definition will be slightly stronger than this.) Obviously, all vertices are covered when the graph is blanketed, and hence C <= B. The blanket time is introduced by Winkler and Zuckerman motivated by applications in Markov estimation and distributed computing. They conjectured B =O(C) and proved B=O(C ln n ). In this paper, we introduce another parameter M motivated by Matthews' theorem and prove 
M/2 <= C <= B = O(( M ln ln n)^2). 
In particular, B = O(C (ln ln n)^2). The lower bound is still valid for the cover time C(\pi) starting from the stationary distribution. We also show that there is a polynomial time algorithm to approximate M within a factor of 2 and so does for C within a factor of O((ln ln n)^2), improving previous bound of O(ln n) of Matthews'.

The cover time, the blanket time, and the Matthews bound

For convex bodies K with $\mathcal {C}^{2}$ boundary in ℝ d , we explore random polytopes with vertices chosen along the boundary of K. In particular, we determine asymptotic properties of the volume of these random polytopes. We provide results concerning the variance and higher moments of this functional, as well as an analogous central limit theorem.

/pdf/an-inscribing-model-for-random-polytopes-552ikty0it.pdf

An Inscribing Model for Random Polytopes

We discuss several questions concerning sum-free sets in groups, raised by Erdős in his survey "Extremal problems in number theory" (Proceedings of the Symp. Pure Math. VIII AMS) published in 1965. 
Among other things, we give a characterization for large sets $A$ in an abelian group $G$ which do not contain a subset $B$ of fixed size $k$ such that the sum of any two different elements of $B$ do not belong to $A$ (in other words, $B$ is sum-free with respect to $A$). Erdős, in the above mentioned survey, conjectured that if $|A|$ is sufficiently large compared to $k$, then $A$ contains two elements that add up to zero. This is known to be true for $k \leq 3$. We give counterexamples for all $k \ge 4$. On the other hand, using the new characterization result, we are able to prove a positive result in the case when $|G|$ is not divisible by small primes.

Sumfree sets in groups: a survey

This is a continuation of our earlier paper on the universality of the eigenvalues of Wigner random matrices. The main new results of this paper are an extension of the results in that paper from the bulk of the spectrum up to the edge. In particular, we prove a variant of the universality results of Soshnikov for the largest eigenvalues, assuming moment conditions rather than symmetry conditions. The main new technical observation is that there is a significant bias in the Cauchy interlacing law near the edge of the spectrum which allows one to continue ensuring the delocalization of eigenvectors.

/pdf/random-matrices-universality-of-local-eigenvalue-statistics-2r2k2j2m2s.pdf

Random matrices: Universality of local eigenvalue statistics up to the edge

We show that any subset of density $ \frac{100}{log_*^{1/4} n} $of an n by n square in $ \mathbb{Z}^2 $ contains an isoceles right-angle triangle with a fixed orientation whose sides are parallel to the axes, for all sufficiently large n.

Van Vu

Papers

The cover time, the blanket time, and the Matthews bound

An Inscribing Model for Random Polytopes

Sumfree sets in groups: a survey

Random matrices: Universality of local eigenvalue statistics up to the edge

On a Question of Gowers