# Program

Random graphs and related random discrete structures lie at the forefront of applied probability and statistics, and are core topics across a wide range of scientific disciplines where mathematical ideas are used to model and understand real-world networks. At the same time, random graphs pose challenging mathematical and algorithmic problems that have attracted attention from probabilists and combinatorialists since at least 1960, following the pioneering work of Erdos and Renyi.
Around the turn of the millennium, as very large data sets became available, several applied disciplines started to realize that many real-world networks, even though they are from various origins, share fascinating features. In particular, many such networks are small worlds, meaning that graph distances in them are typically quite small, and they are scale-free, in the sense that the number of connections made by their elements is extremely heterogeneous. This program is devoted to the study of the probabilistic and statistical properties of such networks. Central tools include graphon theory for dense graphs, local weak convergence for sparse graphs, and scaling limits for the critical behavior of graphs or stochastic processes on them. The program is aimed at pure and applied mathematicians interested in network problems.

**Keywords and Mathematics Subject Classification (MSC)**

**Tags/Keywords**

Structure of/optimization on random graphs

statistics of networks

counting and sampling discrete structures

estimation of network parameters

complexity vs. statistical accuracy

community detection

combinatorial statistics

random processes on graphs

randomized algorithms on random graphs

probabilistic analysis of network algorithms

**Primary Mathematics Subject Classification**

**Secondary Mathematics Subject Classification**