It is easy to see that if a tournament (a complete oriented graph) has a directed cycle then it has a directed 3-cycle. We investigate the analogous question for 3-tournaments, and more generally for oriented 3-graphs.

Cycles are fundamental objects in graph theory. A spanning cycle in a graph is also called a Hamiltonian cycle. The celebrated Dirac's Theorem in 1952 shows that every graph on $n\ge 3$ vertices with minimum degree at least $n/2$ contains a Hamiltonian cycle. In recent years, there has been a strong focus on extending Dirac’s Theorem to hypergraphs. We survey the results along the line and mention some recent progress on this problem. Joint work with Yi Zhao.

What is the probability that the number of triangles in an Erdős–Rényi random graph exceeds its mean by a constant factor? In this talk, I will discuss some recent progress on this problem.

Already the order in the exponent of the tail probability was a long standing open problem until several years ago when it was solved by DeMarco and Kahn, and independently by Chatterjee. We now wish to determine the exponential rate of the tail probability. Thanks for the works of Chatterjee--Varadhan (dense setting) and Chatterjee--Dembo (sparse setting), this large deviations problem reduces to a natural variational problem. We solve this variational problem asymptotically, thereby determining the large deviation rate, which is valid at least for p > 1/n^c for some c > 0.

Based on joint work with Bhaswar Bhattacharya, Shirshendu Ganguly, and Eyal Lubetzky.

We discuss a new setting of algorithmic problems in random graphs, studying the minimum number of queries one needs to ask about the adjacency between pairs of vertices of $G(n,p)$ in order to typically find a subgraph possessing a certain structure. More specifically, given a monotone property of graphs $P$, we consider $G(n,p)$ where $p$ is above the threshold probability for $P$ and look for adaptive algorithms which query significantly less than all pairs of vertices in order to reveal that the property $P$ holds with high probability. In this talk we focus particularly on the properties of containing a Hamilton cycle and containing paths of linear size. The talk is based on joint work with Asaf Ferber, Michael Krivelevich and Benny Sudakov.

The Erdős–Ko–Rado theorem is a central result in extremal set theory which tells us how large uniform intersecting families can be. In this talk, I shall discuss some recent results concerning the 'stability' of this result. One possible formulation of the Erdős–Ko–Rado theorem is the following: if $n \ge 2r$, then the size of the largest independent set of the Kneser graph $K(n,r)$ is $\binom{n-1}{r-1}$, where $K(n,r)$ is the graph on the family of $r$-element subsets of $\{1,\dots,n\}$ in which two sets are adjacent if and only if they are disjoint. The following will be the question of interest. Delete the edges of the Kneser graph with some probability, independently of each other: is the independence number of this random graph equal to the independence number of the Kneser graph itself? I shall discuss an affirmative answer to this question in a few different regimes. Joint work with Bollobás and Raigorodskii, and Balogh and Bollobás.

A system of linear equations with integer coefficients is partition regular if, whenever the natural numbers are finitely coloured, there is a monochromatic solution. The finite partition regular systems were completely characterised by Rado in terms of a simple property of their matrix of coefficients. As a result, finite partition regular systems are very well understood.

Much less is known about infinite systems. In fact, only a very few families of infinite partition regular systems are known. I'll explain a relatively new method of constructing infinite partition regular systems, and describe how it has been applied to settle some basic questions in the area.

A graph is quasirandom if its edge distribution is similar (in a well defined quantitative way) to that of a random graph with the same edge density. Classical results of Thomason and Chung-Graham-Wilson show that a variety of graph properties are equivalent to quasirandomness. On the other hand, in some known proofs the error terms which measure quasirandomness can change quite dramatically when going from one property to another which might be problematic in some applications.

Simonovits and Sós proved that the property that all induced subgraphs have about the expected number of copies of a fixed graph $H$ is quasirandom. However, their proof relies on the regularity lemma and gives a very weak estimate. They asked to find a new proof for this result with a better estimate. The purpose of this talk is to accomplish this.

Joint work with D. Conlon and J. Fox

It has become possible in recent years to provide unconditional lower bounds on the time needed to perform a number of basic computational operations. I will briefly discuss some of the main techniques involved and show how one in particular, the information transfer method, can be exploited to give  time lower bounds for computation on streaming data.

I will then go on to present a simple looking mathematical conjecture with a probabilistic combinatorics flavour that derives from this work.  The conjecture is related to the classic "coin weighing with a spring scale" puzzle but has so far resisted our best efforts at resolution.