Tilings in randomly perturbed graphs: bridging the gap between Hajnal-Szemer\'edi and Johansson-Kahn-Vu
Jie Han, Patrick Morris, Andrew Treglown

TL;DR
This paper determines the number of random edges needed in a dense graph with minimum degree lpha n to almost surely contain a perfect K_r-tiling after random perturbation, bridging the gap between known results for purely random and dense graphs.
Contribution
It establishes thresholds for the number of random edges required in perturbed graphs to guarantee perfect K_r-tilings, connecting previous results for random and dense graphs.
Findings
Number of random edges needed jumps at regular intervals as lpha increases.
Results are asymptotically optimal within these intervals.
Bridges the gap between Johansson-Kahn-Vu and Hajnal-Szemeredi results.
Abstract
A perfect -tiling in a graph is a collection of vertex-disjoint copies of that together cover all the vertices in . In this paper we consider perfect -tilings in the setting of randomly perturbed graphs; a model introduced by Bohman, Frieze and Martin where one starts with a dense graph and then adds random edges to it. Specifically, given any fixed we determine how many random edges one must add to an -vertex graph of minimum degree to ensure that, asymptotically almost surely, the resulting graph contains a perfect -tiling. As one increases we demonstrate that the number of random edges required `jumps' at regular intervals, and within these intervals our result is best-possible. This work therefore closes the gap between the seminal work of Johansson, Kahn and Vu (which resolves the purelyâŠ
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Tilings in randomly perturbed graphs: bridging the gap between HajnalâSzemerĂ©di and JohanssonâKahnâVu
Jie Han111University of Rhode Island, Kingston, RI, USA, [email protected]., Patrick Morris222Freie UniversitĂ€t Berlin, Germany and Berlin Mathematical School, Germany, [email protected]. Research supported by a Leverhulme Trust Study Abroad Studentship (SAS-2017-0529). â and Andrew Treglown333University of Birmingham, United Kingdom, [email protected]. Research supported by EPSRC grant EP/M016641/1.
Abstract
A perfect -tiling in a graph is a collection of vertex-disjoint copies of covering all the vertices in . In this paper we consider perfect -tilings in the setting of randomly perturbed graphs; a model introduced by Bohman, Frieze and Martin [bfm1] where one starts with a dense graph and then adds random edges to it. Specifically, given any fixed we determine how many random edges one must add to an -vertex graph of minimum degree to ensure that, asymptotically almost surely, the resulting graph contains a perfect -tiling. As one increases we demonstrate that the number of random edges required âjumpsâ at regular intervals, and within these intervals our result is best-possible. This work therefore closes the gap between the seminal work of Johansson, Kahn and Vu [jkv] (which resolves the purely random case, i.e., ) and that of Hajnal and SzemerĂ©di [hs] (showing that when the initial graph already houses the desired perfect -tiling).
1 Introduction
A significant facet of both extremal graph theory and random graph theory is the study of embeddings. In the setting of random graphs, one is interested in the threshold for the property that asymptotically almost surely (a.a.s.) contains a fixed (spanning) subgraph . Meanwhile, a classical line of inquiry in extremal graph theory is to determine the minimum degree threshold that ensures a graph contains a fixed (spanning) subgraph . A much studied problem in both the extremal and random settings concerns the case when is a so-called perfect -tiling. In this paper we bridge the gap between the random and extremal models for the problem of perfect clique tilings.
1.1 Perfect tilings in graphs
Given two graphs and , an -tiling in is a collection of vertex-disjoint copies of in . An -tiling is called perfect if it covers all the vertices of . Perfect -tilings are also referred to as -factors, -matchings or perfect -packings. Note that a perfect -tiling is a generalisation of the notion of a perfect matching; indeed, perfect matchings correspond to the case when is a single edge.
One of the cornerstone results in extremal graph theory is the HajnalâSzemerĂ©di theorem [hs] which determines the minimum degree threshold that ensures a graph contains a perfect -tiling.
Theorem 1.1** (Hajnal and Szemerédi [hs]).**
Every graph whose order is divisible by and whose minimum degree satisfies contains a perfect -tiling. Moreover, there is an -vertex graph with that does not contain a perfect -tiling.
Earlier, Corrådi and Hajnal [corradi] proved Theorem 1.1 in the case when . See [short] for a short proof of Theorem 1.1.
Since the proof of Theorem 1.1 there have been many generalisations obtained in several directions. For example, KĂŒhn and Osthus [kuhn2] characterised, up to an additive constant, the minimum degree which ensures that a graph contains a perfect -tiling for an arbitrary graph . Keevash and Mycroft [my] proved an analogue of the HajnalâSzemerĂ©di theorem in the setting of -partite graphs, whilst there are now several generalisations of Theorem 1.1 in the setting of directed graphs (see e.g. [cdkm, treg, forum]). See [survey] for a survey including many of the results on graph tiling. There has also been significant interest in tiling problems in hypergraphs; the survey of Zhao [zsurvey] describes many of the results in the area.
1.2 Perfect tilings in random graphs
Recall that the random graph consists of vertex set where each edge is present with probability , independently of all other choices. In the early 1990s, the problem of determining the threshold for the property that contains a perfect -tiling attracted the attention of ErdĆs (see the appendix of [prob1]). Indeed, as well as raising the general problem, ErdĆs particularly focused on the case when stating that âthe correct answer will be probably about edgesâ, though cautioned that âthe lack of analogs to Tutteâs theorem may cause serious troubleâ. This caution turned out to be well-founded as for a number of years even the case of triangles remained quite stubborn, despite some partial results towards it [kim, k1]. However, in 2008, spectacular work of Johannson, Kahn and Vu [jkv] not only resolved the problem for perfect -tilings, but the general problem of perfect -tilings for all so-called strictly balanced graphs . Below we state their result only in the case of perfect clique tilings.
Theorem 1.2** (Johansson, Kahn and Vu [jkv]).**
Let be divisible by where .
- âą
If then a.a.s. contains a perfect -tiling.
- âą
If then a.a.s. does not contain a perfect -tiling.
1.3 The model of randomly perturbed graphs
Bohman, Frieze and Martin [bfm1] introduced a model which provides a connection between the extremal and random graph settings. In their model one starts with a dense graph and then adds random edges to it. A natural problem in this setting is to determine how many random edges are required to ensure that the resulting graph a.a.s. contains a given graph as a spanning subgraph. For example, the main result in [bfm1] states that for every , there is a such that if we start with an arbitrary -vertex graph of minimum degree and add random edges to it, then a.a.s. the resulting graph is Hamiltonian. This result characterises how many random edges we require for every fixed . Indeed, if then Diracâs theorem implies that we do not require any random edges; that is any -vertex graph of minimum degree is already Hamiltonian. On the other hand, if then the following example implies that we indeed require a linear number of random edges: Let be the complete bipartite graph with vertex classes of size . It is easy to see that if one adds fewer than (random) edges to , the resulting graph is not Hamiltonian.
In recent years, a range of results have been obtained concerning embedding spanning subgraphs into a randomly perturbed graph, as well as other properties of the model; see e.g. [bwt2, benn, bfkm, bhkmpp, bmpp2, das, dudek, joos2, kks2, kst, antoniuk2020high, knierim2019k_r]. The model has also been investigated in the setting of directed graphs and hypergraphs (see e.g. [bhkm, hanzhao, kks1, mm]). Much of this work has focused on the range where the minimum degree of the deterministic graph is linear but with respect to some arbitrarily small constant . In this range, one thinks of the deterministic graph as âhelpingâ to get a certain spanning structure and the observed phenomenon is usually a decrease in the probability threshold of a logarithmic factor, as is the case for Hamiltonicity as above. Recently, there has been interest in the other extreme, where one starts with a minimum degree slightly less than the extremal minimum degree threshold for a certain spanning structure and requires a small âsprinklingâ of random edges to guarantee the existence of the spanning structure in the resulting graph, see e.g. [dudek, nan].
Balogh, Treglown and Wagner [bwt2] considered the perfect -tiling problem in the setting of randomly perturbed graphs. Indeed, for every fixed graph they determined how many random edges one must add to a graph of linear minimum degree to ensure that a.a.s. contains a perfect -tiling. Again we only state their result in the case of perfect clique tilings.
Theorem 1.3** (Balogh, Treglown and Wagner [bwt2]).**
Let . For every , there is a such that if and is a sequence of graphs with and minimum degree then a.a.s. contains a perfect -tiling.
Theorem 1.3, unlike Theorem 1.2, does not involve a logarithmic term. Thus comparing the randomly perturbed model with the random graph model, we see that starting with a graph of linear minimum degree instead of the empty graph saves a logarithmic factor in terms of how many random edges one needs to ensure the resulting graph a.a.s. contains a perfect -tiling. Further, Theorem 1.3 is best-possible in the sense that given any , there is a constant and sequence of graphs where is an -vertex graph with minimum degree at least so that a.a.s. does not contain a perfect -tiling when (see Section 2.1 in [bwt2]). However, as suggested in [bwt2], this still leaves open the question of how many random edges one requires if .
In this paper we give a sharp answer to this question. Before we can state our result we introduce some notation.
Definition 1.4**.**
[Perturbed perfect tiling threshold] Given some , and a graph of order , the perturbed perfect tiling threshold satisfies the following.
- (i)
If , then for any sequence of -vertex graphs with , the graph a.a.s. contains a perfect -tiling.
- (ii)
If , for some sequence of -vertex graphs with , the graph a.a.s. does not contain a perfect -tiling.
If it is the case that every sufficiently large -vertex graph of minimum degree at least contains a perfect -tiling we define . We say the threshold is sharp if there are constants such that (i) remains valid with and (ii) is satisfied for any .
Thus, Theorem 1.1 implies that for all whilst Theorem 1.2 precisely states that (actually Theorem 2.3 in [jkv] and Theorem 3.22(ii) in [jlr] imply this threshold is sharp). Our main result deals with the intermediate cases (i.e. when ).
Theorem 1.5**.**
Let be integers. Then given any ,
[TABLE]
*Moreover, the threshold is sharp. *
Thus, Theorem 1.5 provides a bridge between the HajnalâSzemerĂ©di theorem and the JohanssonâKahnâVu theorem. Notice that the value of demonstrates a âjumpingâ phenomenon; given a fixed the value of is the same for all , however if is just above this interval the value of is significantly smaller.
Note in the case when , Theorem 1.5 is implied by the results from [bwt2]; whilst finalising the paper we learned of a very recent result [nan] concerning powers of Hamilton cycles in randomly perturbed graphs which implies the case when and is even. To help provide some intuition for Theorem 1.5, note that is the threshold for the property that contains a copy of in every linear sized subset of vertices; this property will be exploited throughout the proof. Our proof uses the absorption method, and in particular the novel âabsorption reservoir methodâ introduced by Montgomery [M14a, M19], where we use a robust sparse bipartite graph, which we call a template, in order to build an absorbing structure in our graph. We also use âreachabilityâ arguments, introduced by Lo and Markstöm [LM1], in order to build absorbing structures. We use various probabilistic techniques throughout, such as multi-round exposure, and we appeal to SzemerĂ©diâs regularity lemma in order to obtain an âalmost tilingâ.
The paper is organised as follows. In the next section we introduce some fundamental tools that will be applied in the proof of Theorem 1.5. Section 3 then contains the construction that provides the lower bound on in Theorem 1.5. In Section 4 we give an overview of our proof for the upper bound on in Theorem 1.5, which is given in Section 7 after developing the necessary theory in Section 5 and Section 6. Finally some open problems are raised in the concluding remarks section (Section 8).
Additional Note: Since the paper was first submitted there have been some related results proven. Indeed, Knierim and Su [knierim2019k_r], expanding on work of Nenadov and Pehova [nenadov2018ramsey], considered the so-called Ramsey-TurĂĄn problem for clique tilings. They showed that for any , there exists an such that if is a graph with and independence number less than , then contains a perfect -tiling. This implies Theorem 1.5 for and all as if has minimum degree with as above and then (and hence ) has sublinear independence number. In a different direction, Antoniuk, Dudek, Reiher, RuciĆski and Schacht [antoniuk2020high] explored the appearance of powers of Hamilton cycles in randomly perturbed graphs, building on the work of Nenadov and TrujiÄ [nan]. As the power of a Hamilton cycle is a supergraph of a perfect -tiling, their work gives bounds for the existence of clique tilings. They focus solely on with minimum degree where for some , and they obtain tight results in certain cases. The only range where their implied results on clique tilings is tight with regards to the threshold obtained and the minimum degree condition is the case when and is even in Theorem 1.5, already implied by [nan] and [knierim2019k_r] as mentioned above.
2 Notation and preliminaries
We use standard graph theory notation throughout. In particular we use to denote the number of vertices of a graph . Sometimes we will also write and to denote the number of vertices and edges in respectively. We write to denote the neighbourhood of a vertex . For a subset of vertices , denotes the graph induced by on and we use the shorthand to denote . If we simply write . Further, for disjoint subsets of vertices , denotes the bipartite graph induced by on by considering only the edges of with one endpoint in and the other endpoint in . If is a graph on the same vertex set as we write to denote the graph on vertex set with edge set . We write to be the graph obtained from by deleting any edges that also lie in . One key exception to the use of standard notation is our use of , to denote the complement of with respect to a graph which is not complete, see Definition 6.2.
We write to denote the complete -partite graph with parts of size For a graph on vertices and , we define the blow-up of to be the graph with vertex set , such that and for all and , we have if and only if . Given a set and we denote by the set of all ordered -tuples of elements from , while denotes the set of all (unordered) -element subsets of . At times we have statements such as the following: âChoose constants â. This should be taken to mean that one can choose constants from right to left so that all the subsequent constraints are satisfied. That is, there exist increasing functions for such that whenever for all , all constraints on these constants that are in the proof, are satisfied. Finally, we omit the use of floors and ceilings unless it is necessary, so as not to clutter the arguments.
Throughout, we will deal exclusively with ordered embeddings of graphs, which we also refer to as labelled embeddings. Thus when we refer to an embedding of in , we implicitly fix an ordering on , say and say that there is an embedding of onto an (ordered) vertex set if for all and such that .
In what follows, we introduce the tools that we will use in our proofs. Most of these are well known and so are stated without proof. One exception is Lemma 2.8, which is tailored to our purposes and slightly technical (but follows from well-known techniques nonetheless).
2.1 A deterministic tiling result
Let where is the smallest size of a colour class over all colourings of with colours. The following result of KomlĂłs [komlos] is a crucial tool in the proof of Theorem 1.5. It determines the minimum degree threshold for the property of containing an âalmostâ perfect -tiling.
Theorem 2.1**.**
For every graph and every , there exists such that if is a graph on vertices with , then contains an -tiling which covers all but at most vertices of .
This was later improved to a constant number of uncovered vertices by Shokoufandeh and Zhao [sz], but KomlĂłsâ result suffices for our purposes. We will apply KomlĂłsâ theorem to find an almost perfect -tiling in a reduced graph of our (deterministic) graph from Theorem 1.5; here will be a carefully chosen auxiliary graph (not !). We discuss this further in the proof overview section.
2.2 Regularity
We will use the famous regularity lemma due to Szemerédi [szemeredi]. The lemma and its consequences appeared in the form we give here, in a survey of Komlós and Simonovits [ksregularitysurvey], which we also recommend for further details on the subject. First we introduce some necessary terminology. Let be a bipartite graph with bipartition . For non-empty sets , , we define the density of to be . We say that is -regular for some if for all sets and with and we have
[TABLE]
It is also common, when the underlying graph is clear, to refer to as an -regular pair.
We will use the following two well-known results in our proof. The so-called slicing lemma shows that regularity is hereditary, with slightly weaker parameters.
Lemma 2.2** (Slicing lemma [ksregularitysurvey]*Fact 1.5).**
Let be -regular on parts with density and let . Let and with and . Then is -regular with density at least .
The next lemma is an extremely useful tool, extending the control on the edge count in regular pairs to be able to count the number of embeddings of small subgraphs.
Lemma 2.3** (Counting lemma [ksregularitysurvey]*Theorem 2.1).**
Given , and some fixed graph on vertices, let be a graph obtained by replacing every vertex of with an independent set of size and every edge of with an -regular pair of density at least on the corresponding sets. If , then there are at least embeddings of in so that each is embedded into the set .
We now turn to the regularity lemma, which tells us that there is a way to partition any large enough graph in such a way that the graph induces -regular pairs on almost all of the pairs of parts in this partition. Actually, we apply a variant of the lemma which ensures that, ignoring a small number of edges and a small exceptional set of vertices, all such pairs are -regular.
Lemma 2.4** (Degree form of the regularity lemma [ksregularitysurvey]*Theorem 1.10).**
Let and . Then there is an such that the following holds for every and for every graph on vertices. There exists a partition of and a spanning subgraph of satisfying the following:
; 2. 2.
* and ;* 3. 3.
for each , ; 4. 4.
for all pairs , where , the graph is -regular and has density either [math] or greater than .
The sets are called clusters, the exceptional set and the vertices in are exceptional vertices.
The degree condition (3.) in Lemma 2.4 guarantees that the majority of the edges of lie in . To make this more transparent it is useful to focus on the dense -regular pairs and define the following auxiliary graph. The -reduced graph is as follows: The vertex set of is the set of clusters and for each , is an edge of if the subgraph is -regular and has density greater than . The following then follows easily from Lemma 2.4.
Corollary 2.5**.**
Suppose that are constants. Let be a graph on vertices and . Suppose that has a partition and a subgraph as given by Lemma 2.4 and corresponding -reduced graph . Then .
2.3 Supersaturation
The following phenomenon was first noticed by ErdĆs and Simonovits in their seminal paper [erdHos1983supersaturated]. It states that if there are many copies of a given small subgraph in some host graph, then we can also find many copies of a blow-up in the host graph. It can be proven easily e.g. by induction.
Lemma 2.6**.**
Let , let be some graph on vertices and . Then there exists such that the following holds. Suppose is a graph on vertices with sufficiently large such that there are subsets and contains at least labelled copies of with for . Then contains at least labelled copies of with parts such that and .
2.4 Subgraph counts in random graphs
We first recall Jansonâs inequality (see e.g. [jlr, Theorem 2.14]). Let be a finite set and let be a random subset of such that each element of is included independently with probability . Let be a family of non-empty subsets of and for each , let be the indicator random variable for the event . Thus each is a Bernoulli random variable . Let and . Let , where the sum is over not necessarily distinct ordered pairs . Then Jansonâs inequality states that for any ,
[TABLE]
Consider the random graph on an -vertex set . Note that we can view as with . Following [jlr], for a fixed graph , we define . This parameter helps to simplify calculations of in the context of counting the number of embeddings of the graph in . We will also be interested in the appearance of graphs in where we require some subset of vertices to be already fixed in place. Therefore, for a graph , and some independent444With respect to i.e. subset of vertices , we define
[TABLE]
Note that and for any and independent set . If for a single vertex , we drop the brackets and simply write and . Let us collect some more simple observations concerning and which will be useful later.
Lemma 2.7**.**
The following hold:
Let be some constant, and . Let and , then we have that . 2. 2.
As above, let be some constant, and . Suppose now that and let be the complete graph on vertices with one edge missing and let be one of the endpoints of the missing edge. Then and . 3. 3.
Let , be graphs with vertex subsets , , let and and suppose that . Let be the graph formed by the union of and meeting in exactly one vertex , and let be the graph obtained by taking a disjoint union of and . Then letting , we have that and .
Proof.
For parts 1 and 2, it suffices to consider the case . For part 1, we have a simple calculation. Let be a subgraph of with vertices and edges. As , we obtain
[TABLE]
For part 2 first note that as we have that . Let be a subgraph of . If , the calculation from part 1 gives that . So suppose . Now let us distinguish two cases, depending on whether the vertex is in , where is the vertex in such that is a non-edge. If , we have that
[TABLE]
again using that . Likewise, if , we have that
[TABLE]
where the last inequality follows as is minimised in the range at and . This shows that is bounded as desired.
Part 3 also follows from the definition. Indeed, note that one subgraph of that is a minimiser of the term in the definition of must be a subgraph of or a subgraph of . This ensures . Similarly, one subgraph of that is a minimiser of the term in the definition of must be a subgraph of , a subgraph of , or a subgraph of that contains . This ensures . â
We now apply Jansonâs inequality in order to give a general result about embedding constant sized graphs into . The following lemma provides the basis for a greedy process in which we find some larger (linear size) graph in . We will require that the embedding of our larger graph has certain vertices already prescribed and repeated applications of Lemma 2.8 will then allow us to embed the remaining vertices of the graph in a greedy manner. So it is crucial that we can apply the lemma to any subset of (remaining) indices while avoiding any small enough set of (previously used) vertices from being used.
For future applications, we state and prove the following lemma in the context of -uniform hypergraphs (-graphs), and the definition of extends naturally to -graphs and . Recall that is an -graph on vertices where each -tuple of vertices forms an edge with probability , independent of all other -tuples.
Lemma 2.8**.**
Let , and such that , and . Let be labelled -graphs with distinguished vertex subsets such that , , and for all . Now let be an -vertex set and let be labelled vertex subsets with for all . Finally, suppose there are families of labelled vertex sets such that for each , .
Now suppose that and are such that
[TABLE]
where and with respect to . Then, a.a.s., for any , with and any subset such that and for , there exists some such that there is an embedding (which respects labelling) of in on which maps to and to a labelled set in which lies in .
Note by âlabelledâ here we mean that for all , the vertex in is mapped to the vertex in ; moreover the vertex in is mapped to the vertex in some labelled set from .
Proof.
Let us fix with and a vertex subset as in the statement of the lemma. Let and fix . Note that intersects at most of the elements of for each and so we can focus on a subset of each of at least sets which are all contained in . For each and each labelled subset , let denote the indicator random variable that hosts a labelled copy of where is mapped to . To ease notation sometimes we write instead of . Note that counts the number of suitable embeddings in . (So here if is in of the collections , then there are indicator random variables in this sum corresponding to .)
An easy calculation (using the first part of (2.2)) gives that for large enough . We will show that
[TABLE]
and thus by Jansonâs inequality (2.1), . If , taking a union bound over the (at most ) possible sets and the possible , we have that a.a.s., for all such and ; if , we instead bound both the number of and the number of by and draw the same conclusion. So in both cases a.a.s. for all such and and we are done.
Now it remains to verify (2.3). Firstly let . Then
[TABLE]
To ease notation, let and for , we write if, assuming , , the labelled copies of on and on intersect in at least one edge. We split as follows:
[TABLE]
where is defined analogously to for the random variable .
For integers and , write . Fix . There are ways that two labelled -sets share exactly vertices. Fixing two such -sets, there are at most ways of mapping their vertices into . Let denote the maximum number of edges of a -vertex subgraph of , taken over all . As we explain in the next paragraph, we have that for ,
[TABLE]
Here, we crucially used that any copy of on does not have edges intersecting for . Note that the penultimate inequality follows by definition of . The last inequality follows as for all .
Using the above calculation (and the second part of (2.2)) to compare (2.5) and (2.4), we see that the right hand summand of is less than . We now estimate the left hand summand of (2.5) in a similar fashion. For a fixed , let . We let denote the maximum number of edges of a subgraph of which has vertices disjoint from . We have, similarly to before, that
[TABLE]
Thus,
[TABLE]
So bringing both summands together, (2.3) holds and we are done. â
In its full generality, Lemma 2.8 will be a valuable tool in our proof. However, we will also have instances where we do not need to use the full power of the lemma. For instance, setting , and for all , we recover a more standard application of Jansonâs inequality to subgraph containment which we state below for convenience.
Corollary 2.9**.**
Let , and some fixed labelled graph on vertices. Then there exists such that the following holds. If is a set of vertices, are families of labelled subsets such that and is such that , then a.a.s., for each , there is an embedding of onto a set in , which respects labellings.
3 Lower bound construction for the proof of Theorem 1.5
In this section we give a construction that provides the lower bound in the proof of Theorem 1.5. Our construction is a generalisation of that used for the lower bound in Theorem 1.3 (see Section 2.1 of [bwt2]). We will make use of the following result.
Theorem 3.1** ([jlr], part of Theorem ).**
For every and for every there is a positive constant such that if ,
[TABLE]
Let and be in the statement of Theorem 1.5. Consider any and let such that . Let be divisible by . Suppose is an -vertex graph with vertex classes and such that and , where there are all possible edges in except that is an independent set. So .
Choose sufficiently small so that, if a.a.s. does not contain a -tiling of size . The existence of is guaranteed by Theorem 3.1 since has size linear in .
Observe that any copy of in either contains a in , or uses at least vertices in . Thus, a.a.s., the largest -tiling in has size less than and we are done.
4 Overview of the proof of the upper bound of Theorem 1.5
In this section we sketch some of the ideas in the remainder of our proof of Theorem 1.5. We use the by now well-known absorbing method, which reduces the problem into finding a small absorbing structure on some vertex subset and finding a -tiling that leaves a set of vertices uncovered. The property of the absorbing structure on is that for any small set with , one can find a perfect -tiling in , which will finish the proof.
Let and let be an -vertex graph with . Note that it might be true that both and are -free (a.a.s. for ). Thus, to build even a single copy of , we may have to use both deterministic edges (from ) and random edges (from ). We will use the following partition of the edge set of .
Definition 4.1**.**
For and such that , let be such that and . Then is the complete -partite graph with parts such that and , i.e. . We also define to be , i.e. the complement of on the same vertex set.
Some examples are given in Figure 1. Note that when , is simply an independent set of size and is an -clique. The motivation for this partition comes from the following observation. We can build a copy of in by taking copies of in and then applying Jansonâs inequality to conclude that we can âfill upâ the independent sets in some copy of by s and a and obtain a copy of . With a few more ideas, one can repeatedly apply this naive idea to greedily obtain an almost perfect -tiling (see Theorem 5.1).
To build the absorbing set, we use the reachability arguments introduced by Lo and Markström [LM1]. The main part of the reachability arguments rely on the following notion of reachable paths. Given two vertices , a set of constant size is called a reachable path for if both and contain perfect -tilings. Then we meet the same problem as above, and thus need to build certain structures by deterministic edges and âfill up the gapsâ by random edges. We need much more involved arguments, including building copies of in a few different ways and making sure that we can recover the missing edges by . Moreover, when we cannot prove the reachability between every two vertices and have to pursue a weaker property, namely, building a partition of such that the reachability can be established within each part.
Once we have established the existence of reachable paths, we piece these together to form what we call âabsorbing gadgetsâ (Definition 6.14) and then further combine these absorbing gadgets to define our full absorbing structure in . We use an idea of Montgomery [M14a, M19] in order to define our absorbing structure, using an auxiliary âtemplateâ to dictate how we interweave our absorbing gadgets, which will ensure that the resulting absorbing structure has a strong absorbing property, in that it can contribute to a -tiling in many ways. We will introduce the random edges of only in the last stage, when proving the existence of the full absorbing structure in . Thus, we will first be occupied with finding many reachable paths and absorbing gadgets which use these reachable paths, restricting our attention only to the deterministic edges which will contribute to our eventual absorbing structure.
Our analysis splits into three cases depending on the structure of or equivalently, the values of and . The cases are as follows:
is balanced i.e. and so , 2. 2.
and is not balanced i.e. and , 3. 3.
and is not balanced i.e. .
Examples of each case can be seen in Figure 1.
5 An almost perfect tiling
In this section we study almost perfect tilings and prove Theorem 5.1 below. As is the case throughout, in this almost perfect tiling, the edges of which contribute to the copies of will be copies of as defined in Definition 4.1. We will rely on to then âfill in the gapsâ, providing the missing edges i.e. , to guarantee that each copy of is in fact part of a copy of in . Note that and recall the definition of discussed in Section 2.1. When divides , we have and when does not divide , we have
Thus, the almost perfect tiling result of KomlĂłs, Theorem 2.1, guarantees the existence of an -tiling in which covers almost all the vertices. However, given such a tiling we cannot guarantee that the correct edges appear in in order to extend each copy of in the tiling to a copy of . We aim instead to greedily build a -tiling and guarantee that at each step there are copies of . To achieve this, we use the regularity lemma and apply Theorem 2.1 to the reduced graph of . Then by the counting lemma, each copy of in the reduced graph will provide many copies of in .
Theorem 5.1**.**
Let and . Then there exists such that if and is an -vertex graph with , then a.a.s. contains a -tiling covering all but at most vertices.
Proof.
Apply Lemma 2.4 to with and large, such that . We may assume that is sufficiently large. Note that by Corollary 2.5, the resulting -reduced graph has vertices and satisfies . Let the size of the clusters in the regularity partition be and note that . Now by Theorem 2.1, as is sufficiently large, there exists an -tiling covering all but at most vertices of . Let such that the span disjoint copies of in .
Next, let be the collection of subsets such that there exists some for which intersects each in at least elements (and contains no vertices of clusters from outside of ). Here we say that corresponds to . Moreover, we call a copy of in crossing if it contains precisely one vertex from each cluster in the class .
We claim that a.a.s., every in contains a crossing copy of in . Indeed, fix some and suppose corresponds to . Then there are subsets and clusters such that ,
[TABLE]
and form a copy of in . By Lemma 2.2, for every , we have that is a -regular pair with density at least . Thus by Lemma 2.3 contains at least copies of where in each such copy of precisely one vertex lies in each of ; let denote this collection. Now noting that is a collection of disjoint cliques of size at most , Lemma 2.7 (part 1 and 3) implies that . Also, we have that . Thus for sufficiently large, Corollary 2.9 gives that for every there is a copy of from which hosts a labelled copy of in ; thus the claim is satisfied.
One can now use the claim to greedily build the almost perfect -tiling in . Indeed, initially set . At each step we will add a copy of to whilst ensuring is a -tiling in . Further, at every step we only add a copy of if there is some such that each vertex in lies in a different cluster in (recall each consists of clusters).
Suppose we are at a given step in this process such that there exists some cluster (for some ) that still has at least vertices uncovered by . This in fact implies that every cluster in contains at least vertices uncovered by ; these uncovered vertices correspond precisely to a set . Hence by the above claim there is a crossing copy of in . Add this to . Thus, we can repeat this process, increasing the size of at every step, until we find that for every , all the clusters in have at least vertices covered by .
That is, a.a.s. there is a -tiling in covering all but at most
[TABLE]
vertices, as desired. Note that the first term in the above expression comes from the vertices in clusters from the classes ; the second term comes from those vertices in clusters that were uncovered by . â
Note that one can in fact establish the case in a much simpler way because the copies of that we look for can be completely provided by , see e.g. [jlr, Thorem 4.9].
6 The absorption
The aim of this section is to prove the existence of an absorbing structure in . The main outcomes are Corollaries 6.22,  6.24 and 6.25, which will be used in next section to prove our main result.
The key component of the absorbing structure will be some absorbing subgraph . We will define so that it can contribute to a -tiling in many ways. In fact we will define so that if we remove from and we tile almost all of what remains (Theorem 5.1), then no matter which small set of vertices remains, the properties of allow us to complete this tiling to a full tiling of . There are some complications, and the absorbing structure will have different features depending on the exact values of minimum degree and the size of the cliques that we look to tile with.
Our absorbing subgraph will be comprised of two sets of edges, namely the deterministic edges in and the random edges in . Initially, we will be concerned with finding (parts of) the appropriate subgraph in (Section 6.1). In fact, we will need to prove the existence of many copies of the deterministic subgraphs we want, as we will rely on there being enough of these to guarantee that one of them will match up with random edges in (Section 6.2) to give the desired subgraph. Therefore it is useful throughout to consider, with foresight, the random edges that we will be looking for to complete our desired structure, as this also motivates the form of our deterministic subgraphs.
6.1 The absorbing structure - deterministic edges
The smallest building block in our absorbing graph will be , the complete graph on vertices with one edge missing, say between and . This is useful for the simple reason that it can contribute to a -tiling in two ways, namely for . We introduce the following notation to keep track of the partition of the edges between the deterministic graph and the random graph.
Definition 6.1**.**
Suppose such that . We use the notation
[TABLE]
for not necessarily distinct , to denote the -vertex graph with two distinct distinguished vertices: in the part (which has size ) and in the part (which has size ).
Definition 6.2**.**
Let and consider an -vertex graph with two distinguished vertices and . (Typically we will take as in Definition 6.1.) We then write555Note that our use of the notation is non-standard here. to denote the graph on the same vertex set such that , where we take the non-edge of to be . Thus .
We think of and as all lying on the same vertex set throughout with the two distinguished vertices being defined for all three. The following graph gives the paradigm for how we split the edges of between the deterministic and the random graph.
Definition 6.3**.**
For and such that , let be such that and . Then .
Some examples of and can be seen in Figures 3 and 4. Note that if and are the distinguished vertices of , then for are both copies of the graph from Definition 4.1. Also note that is a disjoint union of -cliques as well as a disjoint copy of . Thus, when , it follows from Lemma 2.7 and Corollary 2.9 that the graph is abundant666Specifically, one can see that any linear sized set in contains a copy of a.a.s.. in when for some large enough . Furthermore, as we will see, the minimum degree condition for along with Lemma 2.6 will imply that there are copies of in . This suggests the suitability of this definition as a candidate for how to partition the edge set of between deterministic and random edges. We remark that the case when is slightly more subtle and we have to adjust our decomposition accordingly. We will discuss this is more detail in the next section.
6.1.1 Reachability
In this subsection, we define reachable paths and show that we can find many of these in our deterministic graph , when the graphs used to define such paths are chosen appropriately. The main results are Proposition 6.6, Proposition 6.7 and Proposition 6.12 which deal with Case 1, 2 and 3 respectively. We first define a reachable path which is a graph which connects together -vertex graphs as follows.
Definition 6.4**.**
Let and let be a vector of -vertex graphs such that each has two distinguished vertices, and . Then an -path is the graph obtained by taking one copy of each and identifying with , for . We call and the endpoints of .
In the case where for some -vertex graph , we use the notation and thus refer to -paths. For , we also define where is as defined in Definition 6.2.
We give some explicit examples of -paths later in Figure 6. In the following, as we look to find embeddings of -paths and larger subgraphs in and , we will always be considering labelled embeddings. Therefore, implicitly, when we define graphs such as the -paths above, we think of these graphs as having some fixed labelling of their vertices. Again, the motivation for the definition of -paths comes from considering , with vertices such that . Indeed, then a -path has two -tilings missing a single vertex; one on the vertices of , and one on the vertices of . Our first step is to find many -paths in the deterministic graph , for an appropriately defined . In particular, we are interested in the images of the endpoints of the paths.
Definition 6.5**.**
Let , and be a vector of -vertex graphs (each of which is endowed with a tuple of distinguished vertices). We say that two vertices in an -vertex graph are -reachable (or -reachable if ) if there are at least distinct labelled embeddings of the -path in such that the endpoints of are mapped to .
As discussed before, the graph from Definition 6.3 will be used to provide deterministic edges for our absorbing structure. That is, we look for -paths in for some appropriate . However, for various reasons there are complications with this approach. Sometimes using a slightly different graph will allow more vertices to be reachable to each other. Also, as is the case below when , it is possible that is not sufficiently common in the random graph . Therefore, we have to tweak the graph in order to accommodate these subtleties. This is the reason for using a vector of graphs as we will see. We will look first at Case 1, when and so contains a copy of . This is too dense to appear in the random graph with the frequency that we require and thus we define as in the following proposition.
Proposition 6.6**.**
Let , such that , and is sufficiently large. Let where , as defined in Definition 6.1, and we consider to be on the same vertex set as with a non-edge between the distinguished vertices of . Likewise, let be the same graph with the labels of the distinguished vertices switched. See Figure 2 for an example of (and which is identical).
Then there exists a such that for any -vertex graph of minimum degree , any pair of distinct vertices in are -reachable where .
Proof.
Let be the distinguished vertices of as defined in Definition 6.1. Fix a pair .
We will show that for any , there are at least labelled embeddings of which map to and to , for some . Once we have established this property, this implies the proposition. Indeed, by symmetry, we can also find embeddings of which map to and to . Set . Thus there are at least
[TABLE]
distinct embeddings of the -path in such that the endpoints are mapped to , as desired. This follows as there are choices for ; at least choices for the copy of containing and ; at least choices for the copy of containing and that are disjoint from the choice of (except for the vertex ).
So let us fix . The proof now follows easily from Lemma 2.6. As , we can express the minimum degree as . Thus any set of at most vertices has at least common neighbours. Therefore we have at least labelled copies of where and . This follows by first choosing and then with the right adjacencies. Thus, by Lemma 2.6 we have labelled embeddings of the blow-up, , of these cliques, crucially within the correct neighbourhoods ( and ) to ensure that together with they give us the required embeddings of . â
Note that an -path has endpoints which are isolated. The other vertices of lie in copies of and these copies are disjoint from each other except for a single pair of s that meet at a singular vertex. See Figure 6 for an example. We now turn to Case 2, as described in Section 4. Here we can use the graph from Definition 6.3. We also use a slight variant of where we redefine the distinguished vertices.
Proposition 6.7**.**
Suppose , , such that is sufficiently large, and . Further, let and be as defined in Definition 6.3 and let be the same graph as with distinguished vertices in distinct777Note that this is possible as we are in the case where the number of parts, of is at least 3. parts of size (see Figure 3).
Then there exists such that for any -vertex of minimum degree , every pair of distinct vertices in are -reachable where .
Proof.
We know that . Therefore every set of at most vertices has a common neighbourhood of size at least . We will appeal to Lemma 2.6 to give us the whole -path in one fell swoop. Let be a graph with vertex set
[TABLE]
and consisting of -cliques on ,
and . We claim that if we can find copies of in such that and for , then we are done. Indeed, consider a blow-up of with parts
[TABLE]
where the parts correspond to the vertices of in the obvious way and the size of each part is indicated by the superscript. Now if we have a copy of in with and for all , then this gives us an embedding of an -path. Indeed for , arbitrarily partition with and and partition with . Then and both give copies of whilst and both give copies of where in all cases the distinguished vertices appear in the first set of the union.
It suffices then, by Lemma 2.6, to find embeddings of in with the and . We can do this greedily. Indeed if we choose the and first, followed by and , then and then the remaining vertices, we are always seeking to choose a vertex in which has at most neighbours which have already been chosen. Thus, by our degree condition, we have at least choices for each vertex with the right adjacencies. To ensure that these choices actually give an embedding of we then discard any set of choices with repeated vertices, of which there are , and thus the conclusion holds as is sufficiently large. â
Consider an -path which we denote (see Figure 6 for an example). It is formed by copies of and which intersect in at most one vertex and such that the endpoints of lie in copies of . Furthermore, note that the endpoints of are in distinct connected components. This will be an important feature when we start to address the random edges of our absorbing structure as it will allow us to use Lemma 2.7 to conclude certain statements about the likelihood of finding our desired random subgraph in . This motivated the introduction of in the previous proposition.
In Case 3, we cannot hope to prove reachability between every pair of vertices. Indeed our minimum degree in this case is and and so it is possible that and is disconnected. Thus, as in [hansolo, han2016complexity], we use a partition of the vertices into âclosedâ parts, where we can guarantee that two vertices in the same part are reachable, with some set of parameters. We adopt the following notation which also allows us to consider different possibilities for what vectors we use for reachability.
Definition 6.8**.**
Let be a set of vectors, such that the entry of each vector in is an -vertex graph endowed with a tuple of distinguished vertices. We say that two vertices in are -reachable if they are -reachable for some .
We say that a subset of vertices in a graph is -closed if every pair of vertices888Note that we do not require the vertices of the -paths which give the reachability to lie in . in is -reachable. We denote999If consists of just one vector , we simply refer to sets being -closed and use to denote the closed neighbourhood of a vertex. by the set of vertices in that are -reachable to .
Thus, in this notation, the conclusion of Proposition 6.6 states that is -closed for all satisfying the given hypothesis (and similarly for Proposition 6.7). Notice that if a set is -closed in a graph it may be the case that two vertices are -reachable whilst two other vertices are -reachable for some distinct , of different lengths.
It will be useful for us to consider the following notion.
Definition 6.9**.**
Let be two sets of vectors as in Definition 6.8. Then
[TABLE]
where is defined to be the set
[TABLE]
That is, comprises of all vectors that lie in , , or that can be obtained by a concatenation of a vector from with a vector from .
As an important example, defining , we have that
[TABLE]
In what follows we will apply the following simple lemma repeatedly.
Lemma 6.10**.**
Let and let be two sets of vectors of -vertex graphs, each of which is endowed with a tuple of distinguished vertices and suppose that and are both finite. Suppose is a sufficiently large -vertex graph and . Suppose there exist , and some subset with such that for every , and are -reachable and and are -reachable. Then and are -reachable for .
Proof.
By the pigeonhole principle, there exists some such that and some , such that for every , and are -reachable and and are -reachable. Suppose has length and has length . Thus, fixing , there are at least pairs of labelled vertex sets and in such that there is an embedding of an -path on mapping endpoints to and an embedding of a -path on the vertices which maps the endpoints to . Of these pairs, at most
[TABLE]
are not vertex disjoint or they intersect . Hence, as is sufficiently large we have at least vertex disjoint pairs which together form an embedding of an -path. As we have at least choices for , this gives that and are -reachable and . â
We now turn to proving reachability in Case 3. The following two lemmas together find the partition we will work on. Similar ideas have been used in [hansolo, han2016complexity].
Lemma 6.11**.**
Suppose and such that , and is sufficiently large. Let and for define , where is as defined in Definition 6.3 with distinguished vertices and .
Then there exists constants such that any -vertex graph of minimum degree can be partitioned into at most parts, each of which is -closed and of size at least .
Proof.
Firstly, observe that there is some such that in every set of at least vertices, there are two vertices which are -reachable. Indeed, fix some arbitrary set of vertices , and for , define . Let be the average. Then we have that
[TABLE]
Thus by Jensenâs inequality. By averaging over all pairs we have that there exists a pair so that both and are in the neighbourhood of at least vertices. That is, .
Therefore there are at least edges in with one endpoint in . Applying Lemma 2.6 this ensures that there is so that there are copies of where the first vertex class lies in . Thus together they form copies of with distinguished vertices and ; so and are -reachable in .
Note also that there is some fixed such that for every . Indeed, this follows as there are at least edges in with one endpoint in . So, by Lemma 2.6, there is a fixed such that there are at least embeddings of which map to . Setting , this implies that there are at least vertices which are -reachable to .
Now let , and for . Set . As in the statement of the lemma, define for values of and note that . We will be interested in -reachability and so we will use the shorthand notation . Let be the maximal integer such that there exists a set of vertices, with and not -reachable for any pair .
Suppose . Then is -closed. As and , the lemma holds in this case.
We also have that from our observations above, so we can assume . Now fix such a set of vertices, . We make the following two observations:
- (i)
Any is in for some from our definition of , as otherwise could be added to give a larger family contradicting the maximality of . Indeed, this follows because two vertices that are not -reachable are certainly not -reachable by definition. 2. (ii)
for every pair . This follows from Lemma 6.10 as otherwise we would have that and are -reachable, a contradiction.
We define for , and . Now for , we have that is -closed. Indeed, if there was a and not reachable, then , is a larger family contradicting the definition of . Thus, the almost form the partition we are looking for except that it remains to consider the vertices in . For these, we greedily add them to the other : We have that for each ,
[TABLE]
Here the second inequality holds due to (i), (ii) and the definition of the ; the final inequality holds by our choice of . Thus, there is a such that , and we add to this , arbitrarily choosing such a if there are multiple choices. Let be the resulting partition.
Applications of Lemma 6.10 show that each is -closed. Indeed suppose, for example, that and are two vertices that lie in and are added to in the process of defining . Then for each , taking , an application of Lemma 6.10 with gives that for any , and are -reachable where and . Another application of Lemma 6.10, this time with then gives that and are -reachable with and . Showing other cases of reachability within each are similar. We are now done since for each ,
[TABLE]
where . â
The rough idea for how to handle Case 3 is to run the same proof as in the other cases on each part of the partition given by Lemma 6.11. The point of Lemma 6.11 is that we recover the reachability within each part, albeit at the expense of allowing a family of possible paths used for reachability. However, in the process, we lose the minimum degree condition within each part. The purpose of the next proposition is to fix this, by adjusting parameters and making the partition coarser. Thus, we recover a minimum degree condition which is not quite as strong as what we had previously but good enough to work with in what follows.
Proposition 6.12**.**
Suppose and such that , and is sufficiently large. Let and let as defined in Definition 6.3 and be the same graph with distinguished vertices in distinct parts of the bipartition101010This is analogous to the graph defined in Proposition 6.7. (see Figure 4). We define the following family of vectors of -vertex graphs (endowed with tuples of vertices):
[TABLE]
where denotes the entry of .
Then for all , there exists constants such that for any -vertex graph with minimum degree there is a partition of into at most parts such that each part satisfies the following:
- (i)
; 2. (ii)
All but at most vertices satisfy ; 3. (iii)
* is -closed. *
Proof.
This is a simple case of adjusting the partition already obtained after applying Lemma 6.11. Let be defined as in the outcome of Lemma 6.11 and let be the partition of obtained, with vertex parts denoted . Fix . We create an auxiliary graph on vertex set where for we have an edge in if and only if there are at least edges in with one endpoint in and one in . Then our new partition in will come from the connected components of . That is, if are the components of , then for , we define and let consist of the with . Then certainly point of the hypothesis is satisfied for all . Also is satisfied. Indeed, suppose there exists , with for at least vertices of . Thus, for such vertices and by averaging there exists some such that and . We average again to conclude that there is some such that , and contains at least vertices which have degree into . This contradicts our definition of as then should be an edge of and thus in the same part of .
Thus it only remains to establish reachability. We begin by proving the following claim which is a slight variation of Lemma 6.10.
Claim 6.13**.**
Let be as defined in Lemma 6.11. Suppose and that there exist (not necessarily disjoint) sets such that for any , and are -reachable and for any , and are -reachable. If there exists at least edges with one endpoint in and one endpoint in , then and are -reachable for some and of length at most .
Indeed letting be the distinguished vertices of , we have, by Lemma 2.6, that there are at least embeddings of into which map to and to for some . By averaging, there exists such that there are embeddings of such that the image of and are -reachable and the image of and are -reachable. By considering the embeddings of , and which join to give an embedding of an -path (that is, ignoring choices of embeddings which are not vertex disjoint), we see that and are -reachable with . This completes the proof of the claim.
Recall the partition . Further consider any part . First suppose for some . Now given any , by Lemma 6.11, and are already -reachable for some . However, does not contain a copy of and so is not a valid vector in the family . We therefore apply Claim 6.13 with , to conclude that and are -reachable for some of length at most . Indeed since sends fewer than edges out to any other part of and , the minimum degree condition on ensures that there are at least edges in and hence edges in allowing Claim 6.13 to be applied.
Next suppose is the union of more than one part from . If and , for and as defined above, we can again apply Claim 6.13 to conclude and are -reachable for some of length at most . Therefore, we just need to establish reachability for vertices such that , with but such that and are in the same component of . If , there is a path of (at most ) edges from to in ; if there is a walk of length in that starts and ends at (i.e. traverse a single edge in ). In both cases we can repeatedly apply Lemma 6.10 to derive that and are -reachable with It is crucial here that we apply Claim 6.13 in all cases to establish the reachability here (even when ) in order to guarantee that the vectors witnessing the reachability contain a copy of and hence indeed lie in . â
We remark that the reason for the introduction of in Proposition 6.12 is two-fold. Firstly, it allows us to establish reachability between parts from Lemma 6.11 which have many edges between them. Moreover, as in Proposition 6.7, we have that for every , if is an -path, then the endpoints of are in distinct connected components on (see Figure 6 for an example), which is something that we will require later.
6.1.2 Absorbing gadgets
In this section, we will focus on larger subgraphs which we look to embed in our graph and which will be used as part of an absorbing structure. These are formed by piecing together the -paths of the previous section and the aim will be to obtain subgraphs with even more flexibility, in that they will be able to contribute to a tiling in many ways. The key definition is a graph which we call an absorbing gadget.
Definition 6.14**.**
Let , let be an -vertex graph and let be a labelled family of vectors of -vertex graphs (with tuples of distinguished vertices). Then an -absorbing gadget is a graph obtained by the following procedure. Take disjoint -paths for and and denote their endpoints by and . Place a copy of on for each . For , identify all vertices and relabel this vertex . Finally relabel as for and let , which we refer to as the base set of vertices for the absorbing gadget.
An example of an absorbing gadget is given in Figure 5. Recall that we always consider to have two distinguished vertices which form the only non-edge of the graph. In the previous section we commented on how a -path with endpoints and has two -tilings covering all but one vertex; the first misses , the other misses . The point of the absorbing gadget is to generalise this property, giving a graph which can use any one of a number of vertices (the base set) in a -tiling. In more detail, suppose and as defined in the previous subsection. Let where each is an element from . Then an -absorbing gadget with base set has the property that for any , there is a -tiling covering precisely . Indeed, we have that for all and , there is a -tiling of the -path which uses111111We label all vertices in this discussion as in Definition 6.14. and not the other endpoint of . Then there is a tiling of the -path which uses , a tiling of the -path for which uses and a copy of on which completes the desired -tiling.
As in the previous subsection, we begin by showing that there are many absorbing gadgets in the deterministic graph. Again, although we are interested in -absorbing gadgets for some consisting of vectors, all of whose entries are , we split the edges of our absorbing gadget and rely on the deterministic graph to provide many copies of a subgraph of the gadget. In particular, we will use here our paradigm , defined in Definition 4.1. The following general proposition allows us to show that we can find many absorbing gadgets if all the vertices which we hope to map the base set to, are reachable to each other.
Definition 6.15**.**
Let . Let be a finite set of vectors, such that each entry of each vector in is an -vertex graph with a tuple of distinguished vertices. We write for the collection of all ordered labelled sets where each is an element from . If consists of a single vector we write . That is, is the ordered labelled (multi)set with each element a copy of .
Proposition 6.16**.**
Let , and let be a finite set of vectors, such that each entry of each vector in is an -vertex graph with a tuple of distinguished vertices.
Then there exists , such that for sufficiently large , if is an -vertex graph with vertex subset such that is -closed, and , then for any set with , there exists some and some -absorbing gadget with base set such that there are at least embeddings of in which map to for .
Proof.
Firstly notice that for a fixed , there is a finite number (i.e. ) of -absorbing gadgets such that and has a base set of size . Let be the set of all such absorbing gadgets, let and set . We claim that there is some such that with and as in the statement of the proposition and of size , there are at least subsets of ordered vertices such that there is an embedding of some in which maps the base set of to and the other vertices to a subset121212In particular, if then not all of the vertices of are used in this embedding. of . Given this claim, the conclusion of the proposition follows easily. Indeed, by averaging we get that there is some and at least ordered subsets of vertices in as above, that correspond to an embedding of . Then setting , we get that there must be at least embeddings of in which map the base set to . Indeed for each such embedding of , the vertex set lies in at most different ordered sets of vertices .
So it remains to find these ordered subsets . We will show that can be generated in a series of steps so that every time we choose some vertices, we have choices. We will use the notation of Definition 6.14. Firstly we select vertices in which we can do in many ways. Now repeatedly find disjoint copies of in and label these such that comprise a copy of for each . In order to do this we repeatedly apply Lemma 2.6 and the degree condition which we can take to be (ignoring any neighbours of vertices that have already been chosen in ). Hence there are choices for these copies of .
Now for and , we have that and are -reachable for some of length say. Thus there are embeddings of an -path in which map the endpoints of to . We ignore those choices of embeddings of which use previously chosen vertices of , of which there are . Similarly, for , and are -reachable for some , so select an embedding of an -path in which maps the endpoints to and has all other vertices disjoint from previously chosen vertices. This gives an embedding of an -absorbing gadget in which maps the base set to , to for , and maps to for . Choosing unused vertices arbitrarily until we have a set of vertices, the claim and hence the proof of the proposition are settled. â
6.2 The absorbing structure - random edges
In this section, we will introduce the edges of and show that contains the absorbing structure we desire. The absorbing structure will be formed by choosing absorbing gadgets rooted on certain prescribed sets of vertices. The absorbing gadgets will be -absorbing gadgets for some consisting of vectors whose entries are all . In order to obtain these absorbing gadgets, we consider the absorbing gadgets of just deterministic edges which we looked at in the previous section and show that with high probability, one of these matches up with random edges to get the required subgraph . We begin by investigating the absorbing gadgets that we look for in the random graph.
6.2.1 Absorbing gadgets in the random graph
Recalling Definitions 4.1 and 6.14, let be a labelled family of vectors of -vertex graphs and suppose that there is an embedding of an -absorbing gadget in which maps the base set of the gadget to some , with . Recalling Definition 6.4, define . Now in order to complete this absorbing gadget into one which has the form that we require, we have to find a labelled embedding of an -absorbing gadget onto the ordered vertex set in . The following lemma will be used to show that there are sufficiently many embeddings in of the necessary s defined as above. It is worth noting that as is uniquely defined by , it is in fact the way that we chose our deterministic absorbing gadgets, that guarantees the following conclusions.
Lemma 6.17**.**
Let and , with and suppose . Suppose is such that:
* if , recalling the definition of from Proposition 6.6;* 2. 2.
* if and , recalling the definition of from Proposition 6.7;* 3. 3.
* if , recalling the definition of from Proposition 6.12.*
Then if is an -absorbing gadget with base set such that , we have that and .
Proof.
We recommend that the reader refers to the examples in Figure 6 to help visualise some of the ideas in this proof. Note that as the endpoints of an -path are isolated, we have that the base set of an absorbing gadget is also an isolated set of vertices and so . Defining as two copies of which meet in a singular vertex, we have that consists of disjoint copies of and disjoint copies of , one for each -path used in . Therefore Lemma 2.7 (1) shows that , and repeated applications of Lemma 2.7 (3) show that and in turn as required.
Case 2 is similar. Here we have that and each of the base vertices of lie in a copy, say , of the graph defined as follows. Take a copy of and a copy of that meet in exactly one vertex, which is one of the vertices of the nonedge in . Furthermore, we have that the base vertex is the other vertex in the nonedge of this copy of . We have that each of the is disconnected from the rest of and an application of Lemma 2.7 (1), (2) and gives that and if . If , then is an isolated vertex and a copy of so we have . Now note that consists of copies of , , and a copy of (in the copy of in ) which intersect each other in at most one vertex. Furthermore, one can view as being âbuilt upâ from these copies in the following way: there is an ordering (starting with ) on these copies of , , and such that, starting with the empty graph and adding these copies in this order, each new copy shares at most one vertex with the previous copies already added, and at the end of the process we obtain . Each time we add a copy, we can apply Lemma 2.7 (3) and then again to add in the (to obtain ). This leads us to conclude that and as required.
In Case 3, let and let us fix some which then defines our . For each , let be the connected component of which contains . Due to the definition of , and in particular the fact that each contains a copy of as defined in Proposition 6.12, we have that for all . Also, for , it can be seen that is a graph obtained by sequentially âgluingâ copies of to vertices of degree and that is a vertex of degree in the resulting graph. Similarly to the previous case, applications of Lemma 2.7 (2) and imply that and if and if , we see that is an isolated vertex, namely itself. Also as before, we have that consists of copies of , , and a copy of which intersect each other in at most one vertex. Thus, introducing the ordering of these copies as in Case 2, we can apply Lemma 2.7 repeatedly to obtain the desired conclusion. â
We will use Lemma 6.17 to prove the existence of our desired absorbing gadgets in . Before embarking on this however, we need to know how we wish our absorbing gadgets (in particular their base sets) to intersect in . This is given by the notion of a template in the following subsection.
6.2.2 Defining an absorbing structure
A template with flexibility is a bipartite graph on vertices with vertex classes and , such that , , and for any , with , the induced graph has a perfect matching. We call the flexible set of vertices for the template. Montgomery first introduced the use of such templates when applying the absorbing method in his work on spanning trees in random graphs [M14a, M19]. There, he used a sparse template of maximum degree , which we will also use. It is not difficult to prove the existence of such templates for large enough probabilistically; see e.g. [M14a, Lemma 2.8] . The idea has since be used by various authors in different settings [FKL16, FN17, Kwan16, HKMP18, nenadov2018ramsey, han2019finding]. We will use a template here as an auxiliary graph in order to build an absorbing structure for our purposes.
Definition 6.18**.**
Let and be a bipartite template with maximum degree and flexibility as defined above. Further, let
[TABLE]
be the set of vectors of length at most whose entries are all .
A (-bounded) absorbing structure of flexibility in a graph consists of a vertex set which we label and and a set of embeddings of absorbing gadgets into . We require the following properties:
- âą
For , setting and , we have that is an embedding of some -absorbing gadget such that and the base set of , which we denote , is mapped to by .
- âą
The embeddings of the absorbing gadgets are vertex disjoint other than the images of the base sets. That is, for all , and for all .
We call the flexible set of the absorbing structure.
Thus the absorbing structure is an embedding of a larger graph which is formed of disjoint absorbing gadgets whose base vertices are then identified according to a template of flexibility . We will refer to the vertices of which are the vertices which feature in the embedding of this larger graph. That is,
[TABLE]
Remark 6.19**.**
If is a -bounded absorbing structure of flexibility , then it has less than vertices in total.
In our proof, we will bound by a constant and look for an absorbing structure on a small linear number of vertices. The key property of the absorbing structure is that it inherits the flexibility of the template that defines it, but in the context of -tilings, as detailed in the following remark.
Remark 6.20**.**
If contains an absorbing structure of flexibility , then for any subset of vertices such that , there is a -tiling in covering precisely .
Indeed given such a , letting be the corresponding indices from , we have that has a perfect matching. The matching then indicates, for each , which vertex of to use in a tiling of the corresponding absorbing gadget. That is, for each , if is âmatchedâ to by the perfect matching, then we take the -tiling covering (which exists by the key property of the absorbing gadget mentioned after Definition 6.14) and then take their union.
6.2.3 The existence of an absorbing structure
In order to prove the existence of an absorbing structure, we must find embeddings of absorbing gadgets in our graph. In the previous section we found many embeddings of certain absorbing gadgets with deterministic edges and thus it remains to find embeddings of complementary absorbing gadgets, using only random edges. Therefore we will turn to Lemma 2.8, which is a general result regarding embeddings in random graphs. However, there is still some work to do in the application of this lemma and the following proposition shows how we can use Lemma 2.8 repeatedly in order to embed a larger graph. We state the proposition in a more general form than just for showing the existence of absorbing structures as we will also use the result at other points in the proof. As the statement of the proposition is somewhat technical, we recommend that the reader sees how it is applied in Corollaries 6.22, 6.24 and 6.25 to help with digesting it.
Proposition 6.21**.**
Let and . Then there exists and such that the following holds for any , and .
Suppose that are labelled graphs with distinguished base vertex sets such that , , and for all . Suppose that such that and for all . Let be an -vertex set, and be subsets such that for each , and defining
[TABLE]
we have that . Finally, suppose that are families of vertex sets such that each contains ordered subsets of of size .
Then a.a.s. there is a set of embeddings such that each embeds a copy of into on with being mapped to and being mapped to a set in which does not intersect . Furthermore for , we have that .
Proof.
Fix and let . The idea here is to greedily extract the desired embeddings, finding them one at a time in . To achieve this, we use the multi-round exposure trick, having a constant number of phases such that in each phase we find a collection of embeddings. At the beginning of each phase we ârevealâ another copy of on the same vertex set and focus only on the indices for which we have not yet found a suitable embedding, showing that in any sufficiently large subset of these indices there is an index for which we can find a suitable embedding. At each phase, we will apply Lemma 2.8 and so we first need to slightly adjust the sets we are considering in order to be in the setting of that lemma.
Firstly let us adjust each so that it has non-base vertices and edges. To each add isolated edges. Then add isolated vertices until the resulting graph has vertices and redefine as the resulting graph. Note that if is such that and for the original as in the statement of the proposition, then these conditions are preserved under the above changes to for each , by Lemma 2.7. We also arbitrarily extend each set in each to get sets of size . As we can extend with any vertices not already in the set, it can be seen that we can have families of size at least for some which we now fix. Clearly, a set of valid embeddings of these new (where the new vertices of are mapped to the new vertices from a set in 131313This will be guaranteed in applications of Lemma 2.8 as the lemma is concerned with labelled embeddings.) will also yield a set of embeddings of the original graphs we were interested in.
Now let us turn to the phases of our algorithm. We will generate in rounds so that with each an independent copy of , where is such that . Note that for any graph , vertex subset , constant and probability , one has that for some constant between and . Likewise, multiplication of the probability by some constant results in multiplication of by some constant factor. Hence, choosing sufficiently large, we can guarantee that if and as in the statement of the proposition, then and with such that . We fix such a and for , we define and . We also define , and .
Now, as discussed, we look to choose embeddings one by one in order to reach the desired conclusion. Therefore, for the sake of brevity, at any point in the argument let us say that an embedding of is valid if it maps to and maps to a set in which is disjoint from and also disjoint from for all indices for which we have already chosen an embedding. Our claim is that a.a.s. (with respect to ) we can repeatedly choose valid embeddings until we have found embeddings for all indices in . We therefore need to show that we never get stuck and that this greedy algorithm always finds a valid embedding. In order to do this, we split the algorithm into phases and rely on the edges of in the phase where we will find valid embeddings. We will show that for all , conditioned on the fact that the algorithm has succeeded so far, we have that a.a.s (with respect to ) the algorithm will succeed for a further phase. The conclusion then follows easily as there are constantly many phases.
So let us analyse the phase and condition on the fact that the process has been successful so far and so there are indices that remain for us to find embeddings for. Let us further fix a specific set141414Note that when we must have that . of indices that remain and some set of already chosen valid embeddings where . By the law of total probability, it suffices to condition on this fixed set of embeddings so far and show that a.a.s. (with respect to ) we can repeatedly find valid embeddings, each time removing the corresponding index from , until there are indices remaining. So let and for , define . We have that as due to our condition on . We then apply Lemma 2.8 to the sets such that , and where and play the role of and respectively. Let us check that the conditions needed for the lemma are satisfied. Indeed, we certainly have that , and . Moreover, when ,
[TABLE]
by our definition of whilst for , . This verifies the conditions in (2.2) in all cases and so we conclude that a.a.s., given any set of at most vertices and any set of indices in such that the sets with are pairwise disjoint, there is an index and a valid embedding of in which avoids . This then implies that the greedy process will suceed throughout this phase. Indeed, we can now initiate with and repeatedly find indices for which we have a valid embedding . We add this embedding to our chosen embeddings, add the vertices of it to and delete the index from . The conclusion that we drew from Lemma 2.8 above asserts that we continue this process until we have indices remaining in , which is precisely what we need. Indeed, for , if we have more than indices in left then by the upper bound on for in taking a maximal set such that are all pairwise disjoint for , we have that . In the final phase when we can simply find embeddings one at a time as . This concludes the proof. â
As corollaries, we can conclude the existence of absorbing structures in . We split the cases here as Case 1 and 2 are much simpler.
Corollary 6.22**.**
Let such that either or and let . There exists and such that if and is an -vertex graph with minimum degree , then for any and any set of vertices , a.a.s. there exists a -bounded absorbing structure in of flexibility , which has flexible set .
Proof.
We look to apply Proposition 6.21 and simply need to establish the hypothesis of the proposition. Consider a bipartite template as in Definition 6.18; recall such a template exists [M14a]. Fix and choose an arbitrary set of vertices which we label . Now towards applying Proposition 6.21, we set and for we define the sets where is as in Definition 6.18. Note that we can set as we start with a template with , so for any set (of at most vertices), there are at most indices such that .
Now, fixing , the collection , which we will use when applying Proposition 6.21, will be obtained from Proposition 6.16. Indeed, this proposition implies, along with Propositions 6.6 and 6.7, that there is some such that the following holds with if (Case 1) and otherwise (Case 2).
Claim 6.23**.**
For any set of at most vertices, there is an -absorbing gadget151515Recall here the definition of from Proposition 6.6, of from Proposition 6.7 and from Definition 4.1. The notation is also defined as in Definition 6.15. such that there are at least embeddings of in which map the base set of the absorbing gadget to .
For each , apply Claim 6.23 with playing the role of to obtain a collection of ordered vertex sets from that combined with each span such an absorbing gadget . For each such embedding of , if we have an ordered -absorbing gadget (in ), on the same vertex set, then we obtain the desired embedding of a -absorbing gadget in , where is a -path. Applying Proposition 6.21 with small enough thus gives us the absorbing structure, upon noticing that the conditions on and are satisfied by Lemma 6.17. â
The third case, when , follows the exact same method of proof. The main difference comes from the fact that we do not have many absorbing gadgets for all small sets of vertices in the deterministic graph but only for sets which lie in one part of the partition dictated by Lemma 6.12. Therefore we look to find an absorbing structure in each part of the partition. Thus when we apply Proposition 6.21, we do so to find all these absorbing structures at once, in order to guarantee that these absorbing structures are disjoint. The conclusion is as follows.
Corollary 6.24**.**
Let be integers, and define , and . Then there exists such that the following holds for all . There exists and such that if and is an -vertex graph with minimum degree , then for any there is a partition of into at most parts with the following properties:
- âą
* for ;*
- âą
;
- âą
* for each ;*
- âą
For any collection of subsets such that for all , there a.a.s. exists a set of -bounded absorbing structures in such that each has flexibility and has flexible set . Furthermore for all .
Proof.
We begin by applying Proposition 6.12 to get a vertex partition with at most parts and in each part we remove any vertex which has internal degree , and add to . The resulting partition is the partition we will use. Choosing in the application of Proposition 6.12 to be less than , we have that the first three bullet points are satisfied. Below we show the last bullet point, and to aid readability we temporarily fix .
Now given a set of we choose a set such that . Further, according to some template as in Definition 6.18, we label according to and according to and identify sets for each according to the neighbourhood of in . As in Corollary 6.22, by Propositions 6.12 and 6.16 there exists some such that for each , fixing the following holds. There is some and some -absorbing gadget such that there are at least embeddings of in which map the base set of to . Each of these embeddings gives a candidate vertex set for which we could embed an -absorbing gadget, say to get a copy of a -absorbing gadget in , with base set , where and . Using Lemma 6.17, we can now apply Proposition 6.21 (provided is sufficiently small) to get the desired embeddings of all the which an absorbing structure as in the statement of the corollary. We in fact apply Proposition 6.21 for all at once which gives the collection of absorbing structures as required. â
Before proving the upper bound in our main result, Theorem 1.5, we give one last consequence of Proposition 6.21 which will be useful for us.
Corollary 6.25**.**
Suppose that and . Then there exists and such that the following holds. Suppose is an -vertex graph with disjoint vertex sets such that , and for all , and is such that . Then a.a.s. in there is a set of disjoint copies so that each copy of contains a vertex of and vertices of .
Proof.
Firstly, let . By the fact that for all , we have that each vertex is in at least distinct copies of in such that the other vertices of each copy lie in , and is contained in the nonedge of each . Thus by Lemma 2.6, there exists some such that each is in copies of with the other vertices of each copy in , and in the part of size in . Let be the collection of -sets of vertices in that, together with , give rise to these copies of containing . Set with an identified vertex in the clique of size in . Thus an ordered embedding in of which maps to and to an ordered set in will give an embedding of in containing and vertices of . By Lemma 2.7 we have that and . Thus, provided is sufficiently small, an application of Proposition 6.21 gives the desired set of embeddings of in . â
7 Proof of the upper bound of Theorem 1.5
In this section we prove the upper bound of Theorem 1.5. Fix some sufficiently large and let be an -vertex graph with . We will show that there exists such that if , then a.a.s. contains a perfect -tiling. Again, we split the proof according to the parameters. We first treat Cases 1 and 2 together (i.e. when or ). Here we avoid many of the technicalities which occur in Case 3 and the main scheme of the proof is clear.
Proof of Cases 1 and 2. Suppose or , and let be chosen such that we can express with each a copy of where and is large enough to be able to draw the desired conclusions in what follows. Now fix where is as in Corollary 6.22 and consider to be the subset generated by taking every vertex in in with probability , independently of the other vertices. With high probability, by Chernoffâs theorem, we have that and for every vertex , . Take an instance of where this is the case and let if is even and for some arbitrary vertex if is odd. Apply Corollary 6.22 to get a -bounded absorbing structure in with flexibility and flexible set . Remark 6.19 implies .
Then letting , we have that . Choose , where is the constant obtained when applying Corollary 6.25 with playing the role of respectively.
Apply Theorem 5.1 to obtain a -tiling in covering all but at most vertices of . Let denote the set of those vertices in uncovered by . Apply Corollary 6.25 to obtain a -tiling in which covers and covers precisely vertices of . Let be the set of those vertices in not covered by . We have that so we can apply Theorem 5.1 to obtain a -tiling in which covers all but at most vertices of .
By Remark 6.20 we know that for any subset of of size , there is a -tiling covering precisely . Thus, is divisible by . Therefore, as the only vertices in uncovered by are those from , there must be a subtiling which covers all but exactly vertices of .
Let be the set of vertices of that are covered by cliques in . Thus and by Remark 6.20 there is a -tiling in covering precisely . Hence, gives a perfect -tiling of as required. â
If , we have to overcome a few technicalities. The idea is to apply Corollary 6.24 and to apply the same approach as above in each of the parts of the resulting partition to find a -tiling. Of course we also have to incorporate the vertices of the exceptional class into copies of cliques in our tiling; this is straightforward using Corollary 6.25. So we cover these vertices first before embarking on tiling the majority of the graph.
More subtle is a problem that arises from divisibility. That is, when we tile each part according to the scheme above, we cannot guarantee that we are left with a subset of the flexible set of the right size to apply the key property of the absorbing structure. Therefore we embed âcrossingâ copies of in our flexible sets in order to resolve this divisibility hurdle at the end of our process. We find these copies in the following manner. Consider the graph . Because of our minimum degree condition and Lemma 2.6, every part contains at least copies of for some . Now let be the graph consisting of a copy of and a copy of joined at a single vertex , say. If we consider and to have the same vertex set so that , then is a copy of . Also note that it follows from Lemma 2.7 that for . We will look for embeddings of in such that the vertex is mapped to one part of the partition and the other vertices lie in another part of the partition.
Proof of Case 3. Suppose , and . Now let be chosen so that we can express with each and a copy of where and is large enough to be able to draw the desired conclusions in what follows.
We use our first copy of to find the crossing copies of discussed above. Apply Corollary 6.24, letting be the outcome of the corollary with input . Choose , where is the constant obtained when applying Corollary 6.25 with playing the roles of respectively. Thus, Corollary 6.24 yields a partition of where and . Note for each . Thus for each and every subset of at least vertices, Lemma 2.6 implies there exists some such that there are at least choices of pairs such that hosts a copy of the graph discussed above.
Therefore, using that with the graph as described above, we can apply Corollary 2.9 to conclude that for any subset of vertices of at least vertices and any , there is a copy of in which has vertices in and one vertex in . Therefore, we can greedily choose copies of so that we have a set of disjoint copies of in such that contains copies of with one vertex in and vertices of . Let and for , where denotes the vertices which feature in cliques in . Note that , and . We will incorporate these into our flexible sets in order to use the copies of that they define to fix divisibility issues that arise in the final stages of the argument.
Now fix where is as in Corollary 6.24 and for each consider to be a subset selected by taking every vertex in with probability , independently of the other vertices. With high probability, by Chernoffâs theorem, we have that and for every vertex , . Therefore, for each , take an instance of where this is the case and let if is even and for some arbitrary vertex if is odd. Apply Corollary 6.24 to get a collection of absorbing structures in such that each has flexibility and flexible set . By Remark 6.19 we have that is such that .
Therefore, setting , we have that for every , and so an application of Corollary 6.25 yields a -tiling in of cliques, each using one vertex of and vertices of . Setting , we have that . So, as in the previous proof, we let , where is obtained from Corollary 6.25 (where and play the roles of and respectively), and we apply Theorem 5.1 to obtain a -tiling in covering all but at most vertices of . Let be the set of vertices from uncovered by and set for each .
Now for each a simple application of Corollary 6.25 yields a -tiling in which covers and uses precisely vertices of . Note that we do not use any vertices of in these cliques. For each let be the vertices of not involved in copies of in . As , we can apply Theorem 5.1 to obtain a -tiling in which covers all but at most vertices of , for each .
Note that we will not use the full tilings in our final tiling. So (ignoring for now the tilings ), it remains to cover the vertices in for each . We do so by means of the following algorithm. We initiate with the as above and set and for all and . Now whilst , remove a clique from , add it to and add its vertices to . Once this process stops, add copies of in to , and add all their vertices in to for . If , repeat this process, setting . Note that when , and there are no cliques which we could add in this process. However, setting we have that , , and are divisible by for each , so we can deduce that the algorithm takes no cliques from and terminates with for all .
Finally, by the key property of the absorbing structure (Remark 6.20), we have that for each , there is a -tiling in covering and thus is the desired perfect -tiling in . â
8 Concluding remarks
In this paper we have almost completely resolved the perfect -tiling problem for randomly perturbed graphs. The only cases that Theorem 1.5 does not resolve is when . Note however for where we know that
[TABLE]
In fact, one can slightly improve the lower bound, giving that . Indeed, as in our lower bound construction (Section 3), take to be complete graph on vertices with a clique of size removed. Letting be the resulting independent set of vertices, if then a.a.s. we have that the number of copies of in is less than, say, by Markovâs inequality, whilst the number of vertices in which do not lie in copies of is at least , as can be seen by a second moment calculation (see e.g. [jlr, Theorem 3.22]). This precludes the existence of a perfect -tiling in , as the average intersection of a clique in such a tiling with the vertex set would be and we cannot tile with a family of cliques whose average size is given the restrictions above. This leaves a gap between the upper and lower bounds and it would be very interesting to resolve the problem for these âboundaryâ cases.
It is also of interest to consider the analogous problem for perfect -tilings for arbitrary graphs . Note that whilst the main result from [bwt2] determines for all graphs and , the problem is still wide open for larger values of . The methods from our paper are likely to be useful for the general problem, though we suspect how âjumpsâ as increases will depend heavily on the structure of . Thus we believe it would be a significant challenge to prove such a general result.
Acknowledgments
Much of the research in this paper was undertaken whilst the authors were visiting the Instituto Nacional de MatemĂĄtica Pura e Aplicada in Rio de Janeiro as part of the Graphs@IMPA thematic program in 2018. We are grateful to IMPA for the nice working environment. We are also extremely grateful to Louis DeBiasio and Shagnik Das for many useful conversations and to the referees for their careful reviews and their suggestions. In particular, we thank one of the referees who brought our attention to the improvement on the lower bound in the concluding remarks.
References
