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[This post is authored by Luca Trevisan. - T.]

Notions of “quasirandomness” for graphs and hypergraphs have many applications in combinatorics and computer science. Several past posts by Terry have addressed the role of quasirandom structures in additive combinatorics. A recurring theme is that if an object (a graph, a hypergraph, a subset of integers, …) is quasirandom, then several useful properties can be immediately deduced, but, also, if an object is not quasirandom then it possesses some “structure” than can be usefully exploited in an inductive argument. The contrapositive statements of such randomness-structure dichotomies are that certain simple properties imply quasirandomness. One can view such results as algorithms that can be used to “certify” quasirandomness, and the existence (or, in some cases, conjectural non-existence) of such algorithms has implications in computer science, as I shall discuss in this post.
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In this lecture, we use topological dynamics methods to prove some other Ramsey-type theorems, and more specifically the polynomial van der Waerden theorem, the hypergraph Ramsey theorem, Hindman’s theorem, and the Hales-Jewett theorem. In proving these statements, I have decided to focus on the ultrafilter-based proofs, rather than the combinatorial or topological proofs, though of course these styles of proof are also available for each of the above theorems.

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I’ve just uploaded to the arXiv my joint paper with Tim Austin, “On the testability and repair of hereditary hypergraph properties“, which has been submitted to Random Structures and Algorithms. In this paper we prove some positive and negative results for the testability (and the local repairability) of various properties of directed or undirected graphs and hypergraphs, which can be either monochromatic or multicoloured.

The negative results have already been discussed in a previous posting of mine, so today I will focus on the positive results. The property testing results here are finitary results, but it turns out to be rather convenient to use a certain correspondence principle (the hypergraph version of the Furstenberg correspondence principle) to convert the question into one about exchangeable probability measures on spaces of hypergraphs (i.e. on random hypergraphs whose probability distribution is invariant under exchange of vertices). Such objects are also closely related to the”graphons” and “hypergraphons” that emerge as graph limits, as studied by Lovasz-Szegedy, Elek-Szegedy, and others. Somewhat amusingly, once one does so, it then becomes convenient to keep track of objects indexed by vertex sets and how they are exchanged via the language of category theory, and in particular using the concept of a natural transformation to describe such objects as exchangeable measures, graph colourings, and local modification rules. I will try to sketch out some of these connections, after describing the main positive results.

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This month I have been at the Institute for Advanced Study, participating in their semester program on additive combinatorics. Today I gave a talk on my forthcoming paper with Tim Austin on the property testing of graphs and hypergraphs (I hope to make a preprint available here soon). There has been an immense amount of progress on these topics recently, based in large part on the graph and hypergraph regularity lemmas; but we have discovered some surprising subtleties regarding these results, namely a distinction between undirected and directed graphs, between graphs and hypergraphs, between partite hypergraphs and non-partite hypergraphs, and between monotone hypergraph properties and hereditary ones.

For simplicity let us first work with (uncoloured, undirected, loop-free) graphs G = (V,E). In the subject of graph property testing, one is given a property {\mathcal P} which any given graph G may or may not have. For example, {\mathcal P} could be one of the following properties:

  1. G is planar.
  2. G is four-colourable.
  3. G has a number of edges equal to a power of two.
  4. G contains no triangles.
  5. G is bipartite.
  6. G is empty.
  7. G is a complete bipartite graph.

We assume that the labeling of the graph is irrelevant. More precisely, we assume that whenever two graphs G, G’ are isomorphic, that G satisfies {\mathcal P} if and only if G’ satisfies {\mathcal P}. For instance, all seven of the graph properties listed above are invariant under graph isomorphism.

We shall think of G as being very large (so |V| is large) and dense (so |E| \sim |V|^2). We are interested in obtaining some sort of test that can answer the question “does G satisfy {\mathcal P}?” with reasonable speed and reasonable accuracy. By “reasonable speed”, we mean that we will only make a bounded number of queries about the graph, i.e. we only look at a bounded number k of distinct vertices in V (selected at random) and base our test purely on how these vertices are connected to each other in E. (We will always assume that the number of vertices in V is at least k.) By “reasonable accuracy”, we will mean that we specify in advance some error tolerance \varepsilon > 0 and require the following:

  1. (No false negatives) If G indeed satisfies {\mathcal P}, then our test will always (correctly) accept G.
  2. (Few false positives in the \varepsilon-far case) If G fails to satisfy {\mathcal P}, and is furthermore \varepsilon-far from satisfying {\mathcal P} in the sense that one needs to add or remove at least \varepsilon |V|^2 edges in G before {\mathcal P} can be satisfied, then our test will (correctly) reject G with probability at least \varepsilon.

When a test with the above properties exists for each given \varepsilon > 0 (with the number of queried vertices k being allowed to depend on \varepsilon), we say that the graph property {\mathcal P} is testable with one-sided error. (The general notion of property testing was introduced by Rubinfeld and Sudan, and first studied for graph properties by Goldreich, Goldwasser, and Ron; see this web page of Goldreich for further references and discussion.) The rejection probability \varepsilon is not very important in this definition, since if one wants to improve the success rate of the algorithm one can simply run independent trials of that algorithm (selecting fresh random vertices each time) in order to increase the chance that G is correctly rejected. However, it is intuitively clear that one must allow some probability of failure, since one is only inspecting a small portion of the graph and so cannot say with complete certainty whether the entire graph has the property {\mathcal P} or not. For similar reasons, one cannot reasonably demand to have a low false positive rate for all graphs that fail to obey {\mathcal P}, since if the graph is only one edge modification away from obeying {\mathcal P}, this modification is extremely unlikely to be detected by only querying a small portion of the graph. This explains why we need to restrict attention to graphs that are \varepsilon-far from obeying {\mathcal P}.

An example should illustrate this definition. Consider for instance property 6 above (the property that G is empty). To test whether a graph is empty, one can perform the following obvious algorithm: take k vertices in G at random and check whether they have any edges at all between them. If they do, then the test of course rejects G as being non-empty, while if they don’t, the test accepts G as being empty. Clearly there are no false negatives in this test, and if k is large enough depending on \varepsilon one can easily see (from the law of large numbers) that we will have few false positives if G is \varepsilon-far from being empty (i.e. if it has at least \varepsilon |V|^2 vertices). So the property of being empty is testable with one-sided error.

On the other hand, it is intuitively obvious that property 3 (having an number of edges equal to a power of 2) is not testable with one-sided error.

So it is reasonable to ask: what types of graph properties are testable with one-sided error, and which ones are not?

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I’ve just uploaded to the ArXiV my paper “Norm convergence of multiple ergodic averages for commuting transformations“, submitted to
Ergodic Theory and Dynamical Systems. This paper settles in full generality the norm convergence problem for several commuting transformations. Specifically, if (X, {\mathcal X},\mu) is a probability space and T_1, \ldots, T_l: X \to X are commuting measure-preserving transformations, then for any bounded measurable functions f_1, \ldots, f_l: X \to {\Bbb R}, the multiple average

\frac{1}{N} \sum_{n=0}^{N-1} \int_X T_1^n f_1 \ldots T_l^n f_l (1)

is convergent in the L^2(X) norm topology (and thus also converges in probability). The corresponding question of pointwise almost everywhere convergence remains open (and quite difficult, in my opinion). My argument also does not readily establish a formula as to what this limit actually is (it really establishes that the sequence is Cauchy in L^2 rather than convergent).

The l=1 case of this theorem is the classical mean ergodic theorem (also known as the von Neumann ergodic theorem). The l=2 case was established by Conze and Lesigne. The higher l case was partially resolved by Frantzikinakis and Kra, under the additional hypotheses that all of the transformations T_i, as well as the quotients T_i T_j^{-1}, are ergodic. The special case T_j=T^j was established by Host-Kra (with another proof given subsequently by Ziegler). Another relevant result is the Furstenberg-Katznelson theorem, which asserts among other things when f_1=\ldots=f_l is non-negative and not identically zero, then the inner product of the expression (1) with f has a strictly positive limit inferior as N \to \infty. This latter result also implies Szemerédi’s theorem.

It is also known that the Furstenberg-Katznelson theorem can be proven by hypergraph methods, and in fact my paper also proceeds by a hypergraph-inspired approach, although the language of hypergraphs is not explicitly used in the body of the argument. (In contrast to the work of Host-Kra and Ziegler, no nilsystems appear in the proof.)

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