Sparse, highly connected, random-like graphs are currently a focal point in discrete mathematics, theoretical computer science and network science, motivated by the insight that networks of this type are ubiquitous in computing, biology, economics, physics, social science, etc., and by the theoretical challenges of this setting. Random graph models used in statistical modelling of real-world networks include the Molloy--Reed model for scale-free graphs and the Watts--Strogatz model (designed to simultaneously exhibit the small-world phenomenon and formation of hubs, cited 43000 times). Barab\'asi devotes a chapter of his classic network science book to robustness of random graphs, specifically addressing robustness against adversarial attack.
The aim of the project RobSparseRand was to study robustness in sparse graphs, and specifically in random graphs. In our context, robustness can be understood as Ramsey-type properties, or the presence of specific structures, such as paths and cycles. More specifically, the aim is to solve important open problems in Ramsey theory, by advancing the essential tools in random graph theory (sparse regularity framework, concentration bounds, embedding techniques) and illuminating potentially useful classes of expander graphs.