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DataBank, Bodleian Libraries, University of Oxford
Doctoral thesis . 2017
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Problems in extremal and probabilistic combinatorics

Authors: Morrison, N; Noel, J;

Problems in extremal and probabilistic combinatorics

Abstract

In this thesis we consider some problems in extremal and probabilistic combinatorics. In Chapter 2 we determine the maximum number of induced cycles that can be contained in a graph on n ≥ n0 vertices, and show that there is a unique graph that achieves this maximum. This answers a question of Tuza. Let Qd denote the hypercube of dimension d. Given d ≥ m, a spanning subgraph G of Qd is said to be (Qd,Qm)-saturated if it does not contain Qm as a subgraph but adding any edge of E(Qd) \ E(G) creates a copy of Qm in G. In Chapter 3, we show that for every fixed m ≥ 2 the minimum number of edges in a (Qd,Qm)-saturated graph is Θ(2d). This answers a question of Johnson and Pinto. We also answer another question of Johnson and Pinto about weak saturation. Given graphs F and H, a spanning subgraph G of F is said to be weakly (F,H)-saturated if the edges of E(F) \setminus E(G) can be added to G one at a time so that each additional edge creates a new copy of H. We determine the minimum number of edges in a weakly (Qd,Qm)-saturated graph for all d ≥ m ≥ 1. More generally, we determine the minimum number of edges in a subgraph of the d-dimensional grid Pk_d which is weakly saturated with respect to 'axis aligned' copies of a smaller grid Prm. In Chapter 4 we consider the r-neighbour bootstrap process in the hypercube. This process starts with an initial set A0 of infected vertices in a graph G and, at each step of the process, a healthy vertex becomes infected if it has at least r infected neighbours (once a vertex becomes infected, it remains infected forever). If every vertex of G eventually becomes infected, then we say that A0 percolates. We prove a conjecture of Balogh and Bollobás which says that, for fixed r and d tending to infinity, every percolating set in the d-dimensional hypercube has cardinality at least 1+o(1)/r (d choose r-1). We also prove an analogous result for multidimensional rectangular grids. Our proofs exploit a connection between bootstrap percolation and weak-saturation. In addition, we improve on the best known upper bound for the minimum size of a percolating set in the hypercube. In particular, when r=3, we determine the exact cardinality of a minimum percolating set in the d-dimensional hypercube, for all d ≥ 3. Finally, we consider a more general bootstrap process in a hypergraph setting. Given an r-uniform hypergraph 𝓗, the 𝓗-bootstrap process starts with an initial set of infected vertices of 𝓗 and, at each step, a healthy vertex becomes infected if there exists a hyperedge of 𝓗 in which it is the only healthy vertex. The initial set of infected vertices is said to percolate if every vertex of 𝓗 is eventually infected. In Chapter 5, for fixed r and large d, we obtain a sharp threshold for the probability that a p-random set of vertices in a q-random subhypergraph of 𝓗 percolates when p,q= Θ(d-1/(r-1)) and 𝓗 is any nearly d-regular r-uniform hypergraph with at most dO(1) vertices which satisfies certain 'codegree' conditions. As it turns out, for this wide class of hypergraphs, the threshold depends only on r and not on the underlying structure of the hypergraph. We apply this result to obtain a sharp threshold for a variant of the graph bootstrap process for strictly $2$-balanced graphs. This result generalises a theorem of Korándi, Peled and Sudakov and the proof involves an application of the differential equations method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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