Abstract. We study conditions under which an edge-colored hypergraph has a particular substructure that contains more than the trivially guaranteed number of monochromatic edges. Our main result solves this problem for perfect matchings under minimum degree conditions. This answers recent questions of Gishboliner, Glock, and Sgueglia and of Balogh, Treglown, and Zárate-Guerén.

We study the emergence of loose Hamilton cycles in subgraphs of random hypergraphs. Our main result states that the minimum d-degree threshold for loose Hamiltonicity relative to the random k-uniform hypergraph Hk(n, p) coincides with its dense analogue whenever p ≥ n−(k-1)/2+o(1). The value of p is approximately tight for d > (k + 1)/2. This is particularly interesting because the dense threshold itself is not known beyond the cases when d ≥ k - 2.

Given a family of graphs $\mathcal{F}$, we prove that the normalized edit distance of any given graph $\Gamma$ to being induced $\mathcal{F}$-free is estimable with a query complexity that depends only on the bounds of the Frieze--Kannan regularity lemma and on a removal lemma for $\mathcal{F}$.

We show that for any coloring of the edges of the complete bipartite graph $K_{n,n}$ with three colors there are five disjoint monochromatic cycles which together cover all but $o(n)$ of the vertices. In the same situation, 18 disjoint monochromatic cycles together cover all vertices.

There has been substantial interest in estimating the value of a graph parameter, i.e., of a real function defined on the set of finite graphs, by sampling a randomly chosen substructure whose size is independent of the size of the input. Graph parameters that may be successfully estimated in this way are said to be testable or estimable, and the sample complexity q_z=q_z(epsilon) of an estimable parameter z is the size of the random sample required to ensure that the value of z(G) may be estimated within error epsilon with probability at least 2/3. In this paper, we study the sample complexity of estimating two graph parameters associated with a monotone graph property, improving previously known results. To obtain our results, we prove that the vertex set of any graph that satisfies a monotone property P may be partitioned equitably into a constant number of classes in such a way that the cluster graph induced by the partition is not far from satisfying a natural weighted graph generalization of P}. Properties for which this holds are said to be recoverable, and the study of recoverable properties may be of independent interest.