Contraction and Deletion Blockers for Perfect Graphs and $H$-free Graphs
\"Oznur Ya\c{s}ar Diner, Dani\"el Paulusma, Christophe Picouleau, and Bernard Ries

TL;DR
This paper investigates the computational complexity of reducing graph parameters like chromatic, clique, and independence numbers using edge contractions and vertex deletions within perfect and H-free graphs.
Contribution
It characterizes the complexity of parameter reduction problems for specific graph classes and operations, extending understanding of graph modification problems.
Findings
Complexity results for contraction and deletion operations on perfect graphs.
Extension of complexity analysis to H-free graphs.
Identification of cases where parameter reduction is computationally feasible or hard.
Abstract
We study the following problem: for given integers , and graph , can we reduce some fixed graph parameter of by at least via at most graph operations from some fixed set ? As parameters we take the chromatic number , clique number and independence number , and as operations we choose the edge contraction ec and vertex deletion vd. We determine the complexity of this problem for and and for a number of subclasses of perfect graphs. We use these results to determine the complexity of the problem for and and restricted to -free graphs.
| Contraction Blocker() | Deletion Blocker() | |||
|---|---|---|---|---|
| Class | ||||
| tree | P | P | P [3, 13] | P |
| bipartite () | NP-h | P | P [3, 13] | P |
| cobipartite | : NP-c | NP-c; fixed: P | P | P [13] |
| cograph | P | P | P [3] | P |
| split | NP-c; fixed: P | NP-c; fixed: P | NP-c; fixed: P [13] | NP-c; fixed: P [13] |
| interval | P | P | ||
| chordal | NP-c | : NP-c | NP-c | : NP-c |
| -free perfect & | : NP-c | |||
| perfect | : NP-h | : NP-h | NP-c | : NP-c |
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11institutetext: Kadir Has University, Istanbul, Turkey [email protected] 22institutetext: Durham University, Durham, UK, [email protected] 33institutetext: CNAM, Laboratoire CEDRIC, Paris, France [email protected] 44institutetext: University of Fribourg, Fribourg, Switzerland, [email protected]
Contraction and Deletion Blockers
for Perfect Graphs and -free Graphs††thanks: A number of results in this paper have appeared in extended abstracts of the proceedings of CIAC 2016 [16], ISCO 2016 [37], LAGOS 2017 [36] and TAMC 2017 [38].
Öznur Yaşar Diner Author supported by Marie Curie International Reintegration Grant PIRG07/GA/2010/268322.11
Daniël Paulusma Author supported by EPSRC (EP/K025090/1) and the Leverhulme Trust (RPG-2016-258).22
Christophe Picouleau 33
Bernard Ries 44
Abstract
We study the following problem: for given integers , and graph , can we reduce some fixed graph parameter of by at least via at most graph operations from some fixed set ? As parameters we take the chromatic number , clique number and independence number , and as operations we choose the edge contraction ec and vertex deletion vd. We determine the complexity of this problem for and and for a number of subclasses of perfect graphs. We use these results to determine the complexity of the problem for and and restricted to -free graphs.
1 Introduction
A typical graph modification problem aims to modify a graph , via a small number of operations from a specified set , into some other graph that has a certain desired property, which usually describes a certain graph class to which must belong. In this way a variety of classical graph-theoretic problems is captured. For instance, if only vertex deletions are allowed and must be an independent set or a clique, we obtain the Independent Set or Clique problem, respectively.
Now, instead of fixing a particular graph class , we fix a certain graph parameter . That is, for a fixed set of graph operations, we ask, given a graph , integers and , whether can be transformed into a graph by using at most operations from , such that . The integer is called the threshold. Such problems are called blocker problems, as the set of vertices or edges involved “block” some desirable graph property, such as being colourable with only a few colours. Identifying the part of the graph responsible for a significant decrease of the parameter under consideration gives crucial information on the graph.
Blocker problems have been given much attention over the last few years, see for instance [2, 3, 4, 13, 39, 40, 43, 45]. Graph parameters considered were the chromatic number, the independence number, the clique number, the matching number, the weight of a minimum dominating set and the vertex cover number. So far, the set always consisted of a single graph operation, which was a vertex deletion, edge deletion or an edge addition. In this paper, we keep the restriction on the size of by letting consist of either a single vertex deletion or, for the first time, a single edge contraction. As graph parameters we consider the independence number , the clique number and the chromatic number .
Before we can define our problems formally, we first need to give some terminology. The contraction of an edge of a graph removes the vertices and from , and replaces them by a new vertex made adjacent to precisely those vertices that were adjacent to or in (neither introducing self-loops nor multiple edges). We say that can be -contracted or -vertex-deleted into a graph , if can be modified into by a sequence of at most edge contractions or vertex deletions, respectively. We let denote the (fixed) graph parameter; as mentioned, in this paper belongs to .
We are now ready to define our decision problems in a general way:
Contraction Blocker()
* Instance:*
a graph and two integers
Question:
can be -contracted into a graph such that ?
Deletion Blocker()
* Instance:*
a graph and two integers
Question:
can be -vertex-deleted into a graph such that ?
If we remove from the input and fix it instead, then we call the resulting problems -Contraction Blocker() and -Deletion Blocker(), respectively.
-Contraction Blocker()
* Instance:*
a graph and an integer
Question:
can be -contracted into a graph such that ?
-Deletion Blocker()
* Instance:*
a graph and an integer
Question:
can be -vertex-deleted into a graph such that ?
The goal of our paper is to increase our understanding of the complexities of Contraction Blocker() and Deletion Blocker() fo . In order to do so, we will also consider the problems -Contraction Blocker() and -deletion blocker().
1.1 Known Results and Relations to Other Problems
It is known that Deletion Blocker() is polynomial-time solvable for bipartite graphs, as proven both by Bazgan, Toubaline and Tuza [3] and Costa, de Werra and Picouleau [13]. The former authors also proved that Deletion Blocker() is polynomial-time solvable for cographs and graphs of bounded treewidth. The latter authors also proved that for , Deletion Blocker() is polynomial-time solvable for cobipartite graphs. Moreover, they showed that for , Deletion Blocker() is NP-complete for the class of split graphs, but becomes polynomial-time solvable for this graph class if is fixed.
By using a number of example problems we will now illustrate how the blocker problems studied in this paper relate to a number of other problems known in the literature. As we will see, this immediately leads to new complexity results for the blocker problems.
1. Hadwidger Number and Club Contraction. The Contraction Blocker() problem generalizes the well-known Hadwiger Number problem, which is that of testing whether a graph can be contracted into the complete graph on vertices for some given integer . Indeed, we obtain the latter problem from the first by restricting instances to instances where and . Note that the diameter and independence number of are both equal to 1. Hence, one can also generalize Hadwiger Number in another way: the Club Contraction problem (see e.g. [21]) is that of testing whether a graph can be -contracted into a graph with diameter at most for some given integers and . As such, Contraction Blocker() can be seen as a natural counterpart of Club Contraction.
2. Graph transversals. Blocker problems generalize so-called graph transversal problems. To explain the latter type of problems, for a family of graphs , the -transversal problem is to test if a graph can be -vertex-deleted, for some integer , into a graph that has no induced subgraph isomorphic to a graph in . For instance, the problem -transversal is the same as Vertex Cover. Here are some examples of specific connections between graph transversals and blocker problems.
- •
Let be the family of all complete graphs on at least two vertices. Then -transversal is equivalent to Deletion Blocker() restricted to instances with .
- •
In our paper we will prove that for a graph with at least one edge and an integer , the instance is a yes-instance of Deletion Blocker() if and only if is a yes-instance of Vertex Cover.
- •
The Odd Cycle Transversal problem is to test whether a given graph can be made bipartite by removing at most vertices for some given integer . This problem is NP-complete [31], and it is equivalent to Deletion Blocker() for instances where .
- •
The -Transversal or -Cover problem [13] is to decide whether a graph contains a set that intersects each maximum set satisfying some specified property by at least vertices. For instance, if the property is being an independent set, -Transversal is equivalent to 1-Deletion Blocker().
3. Bipartite Contraction. The problem Bipartite Contraction is to test whether a graph can be made bipartite by at most edge contractions. Heggernes et al. [26] proved that this problem is NP-complete. It is readily seen that -Contraction Blocker() and Bipartite Contraction are equivalent for graphs of chromatic number 3.
4. Maximum induced bipartite subgraphs. The Maximum Induced Bipartite Subgraph problem is to decide if a given graph contains an induced bipartite subgraph with at least vertices for some integer . Addario-Berry et al.[1] proved that this problem is NP-complete for the class of 3-colourable perfect graphs. We observe that, for 3-colourable graphs, -Deletion Blocker() is equivalent to Maximum Induced Bipartite Subgraph.
5. Cores. The two problems -Deletion Blocker() and -Deletion Blocker() are equivalent to testing whether the input graph contains a set of of size at most that intersects every maximum independent set or every maximum clique, respectively. If , these two problems become equivalent to testing whether the input graph contains a vertex that is in every maximum independent set, or in every maximum clique, respectively. In particular, the intersection of all maximum independent sets is known as the core of a graph. Properties of the core have been well studied (see, for example, [25, 29, 30]). In particular, Boros, Golumbic and Levit [7] proved that computing if the core of a graph has size at least is co-NP-hard for every fixed . Taking gives co-NP-hardness of -Deletion Blocker().
6. Critical vertices and edges. The restriction has also been studied when . A vertex of a graph is critical if its deletion reduces the chromatic number of by 1. An edge of a graph is critical or contraction-critical if its deletion or contraction, respectively, reduces the chromatic number of by 1. The problems Critical Vertex, Critical Edge and Contraction-Critical are to test if a graph has a critical vertex, critical edge or contraction-critical edge, respectively. We note that the first two problems are the restrictions of Contraction Blocker() and Deletion Blocker() to instances where . Complexity dichotomies exist for each of the three problems on -free graphs, and moreover the latter two problems are shown to be equivalent [36]. Graphs with a critical (or equivalently contraction-critical) edge are also called colour-critical (see, for instance, [41]).
Due to links to problems as the ones above, it is of no surprise that many results for blocker problems are known implicitly in the literature already in various settings. For example, Belmonte et al. [5] proved that -Contraction Blocker(), where denotes the maximum vertex-degree, is NP-complete even for split graphs. We make use of several known complexity results for some of the related problems stated above for proving our results.
1.2 Our Results
In Section 1.1 we mentioned that Deletion Blocker() is known to be NP-complete for even when restricted to special graph classes. Non-surprisingly, Contraction Blocker() is NP-complete for as well (this follows from our results in Section 8, but it is also easy to show this directly).
Due to the above, it is natural to restrict inputs to some special graph classes in order to obtain tractable results and to increase our understanding of the computational hardness of the problems. Note that it is not always clear whether Contraction Blocker() and Deletion Blocker() belong to NP when restricted to a graph class . However, when is closed under edge contraction or vertex deletion, respectively, and can be verified in polynomial time, then membership of NP holds: we can take as certificate the sequence of edge contractions or vertex deletions, respectively.
We present our results in two parts.
Part I. In the first part of our paper we focus on the class of perfect graphs and a number of well-known subclasses of perfect graphs. Most of these classes are not only closed under vertex deletion but also under edge contraction. This enables us to get unified results for the cases and (note that holds by definition of a perfect graph). Another reason for considering subclasses of perfect graphs is that , , can be computed in polynomial time for perfect graphs; Grötschel, Lovász, and Schrijver [23] proved this for and thus for , whereas the result for follows from combining this result with the fact that perfect graphs are closed under complementation. This helps us with finding tractable results or at least with obtaining membership of NP (if in addition the subclass under consideration is closed under edge contraction or vertex deletion).
Table 1 gives an overview of the known results and our new results for the classes of perfect graphs we consider. We have unified results for the cases and even for the perfect graph classes in this table that are not closed under edge contraction, namely the classes of bipartite graphs; -free perfect graphs with clique number 3; and the class of perfect graphs itself. As the class of perfect graphs is not closed under edge contraction we could for perfect graphs only deduce that the three contraction blocker problems are NP-hard (even if ). As the class of cographs coincides with the class of -free graphs (where denotes the -vertex path) and split graphs are -free, the corresponding rows in Table 1 show a complexity jump of all our problems for -free graphs from to . Recall also from Section 1.1 that the Hadwiger Number problem is a special case of Contraction Blocker()) As such, our polynomial-time result in Table 1 for Contraction Blocker() restricted to cographs generalizes a result of Golovach et al. [21], who proved that the Hadwiger Number problem is polynomial time solvable on cographs.
Part II. In the second part of our paper we give several dichotomy results. First we give, for , complete classifications of Deletion Blocker() and Contraction Blocker() depending on the size of , that is, we prove the following theorem.
Theorem 1.1
The following six dichotomies hold:
- (i)
Contraction Blocker()* is polynomial-time solvable for graphs with and 1-Contraction Blocker() is NP-complete for graphs with ;*
**
- (ii)
Contraction Blocker()* is polynomial-time solvable for graphs with and -Contraction Blocker() is NP-complete for graphs with ;*
**
- (iii)
Contraction Blocker()* is polynomial-time solvable for graphs with and -Contraction Blocker() is NP-complete for graphs with ;*
**
- (iv)
Deletion Blocker()* is polynomial-time solvable for graphs with and*
-Deletion Blocker()* is NP-complete for graphs with ;*
**
- (v)
Deletion Blocker()* is polynomial-time solvable for graphs with and*
-Deletion Blocker()* is NP-complete for graphs with ;*
**
- (vi)
Deletion Blocker()* is polynomial-time solvable for graphs with and*
-Deletion Blocker()* is NP-complete for graphs with ;*
In particular we extend the hardness proof of Theorem 1.1 (iii) in order to obtain the hardness result for -free perfect graphs with in Table 1. We note that some of the results in Table 1, such as this result, may at first sight seem somewhat arbitrary. However, we need the result for -free perfect graphs with and other results of Table 1 to prove our other results of the second part of our paper. Namely, by combining the results for subclasses of perfect graphs with other results, we obtain complexity dichotomies for our six blockers problems restricted to -free graphs, that is, graphs that do not contain some (fixed) graph as an induced subgraph. These dichotomies are stated in the following summary; here, is the -vertex path, is the triangle, and the paw is the triangle with an extra vertex adjacent to exactly one vertex of the triangle, whereas denotes the induced subgraph relation and denotes the disjoint union of two vertex disjoint graphs.
Theorem 1.2
Let be a graph. Then the following holds:
- (i)
If , then Deletion Blocker() is polynomial-time solvable for -free graphs, otherwise it is NP-hard or co-NP-hard for -free graphs.
**
- (ii)
If , then Contraction Blocker() is polynomial-time solvable for -free graphs, otherwise it is NP-hard for -free graphs.
**
- (iii)
f , then Deletion Blocker() is polynomial-time solvable for -free graphs; otherwise it is NP-hard or co-NP-hard for -free graphs.
**
- (iv)
Let . If or , then Contraction Blocker) is polynomial-time solvable for -free graphs, otherwise it is NP-hard or co-NP-hard for -free graphs.
**
- (v)
If or , then Deletion Blocker is polynomial-time solvable for -free graphs, otherwise it is NP-hard or co-NP-hard for -free graphs.
**
- (vi)
If , then Contraction Blocker is polynomial-time solvable for -free graphs, otherwise it is NP-hard or co-NP-hard for -free graphs.
Statements (i), (ii), (iii), (v), (vi) of Theorem 1.2 correspond to complete complexity dichotomies, whereas there is one missing case in statement (iv). In particular we note that statements (v) and (vi) do not coincide for disconnected graphs . We also observe from Theorem 1.2 (i) that Deletion Blocker() is computationally hard for triangle-free graphs; in fact we will show co-NP-hardness even if . This in contrast to the problem being polynomial-time solvable for bipartite graphs, as shown in [3, 13] (see also Table 1).
1.3 Paper Organization
Section 2 contains notation and terminology.
Sections 3–7 contain the results mentioned in Part I. To be more precise, Section 3 contains our results for cobipartite graphs, bipartite graphs and trees. In Sections 4 and 5, we prove our results for cographs and split graphs, respectively. In Section 5 we also show that our NP-hardness reduction for split graphs can be used to prove that the three contraction blockers problems, restricted to split graphs, are W[1]-hard when parameterized by . The latter result means that for split graphs these problems are unlikely to be fixed-parameter tractable with parameter . In Sections 6 and 7 we prove our results for interval graphs and chordal graphs, respectively.
Sections 8 and 9 contain the results mentioned in Part II. In Section 8 we first prove dichotomies for the three contraction blocker and three vertex blocker problems when we classify on basis of the size of . In the same section, we modify the hardness construction for -Contraction Blocker() to prove that -Contraction Blocker() is NP-complete even for -free perfect graphs with . In Section 9 we prove Theorem 1.2.
Section 10 contains a number of open problems and directions for future research.
2 Preliminaries
We only consider finite, undirected graphs that have no self-loops and no multiple edges; we recall that when we contract an edge no self-loops or multiple edges are created. We refer to [14] or [47] for undefined terminology and to [15] for more on parameterized complexity.
Let be a graph. For a subset , we let denote the subgraph of induced by , which has vertex set and edge set . We write if a graph is an induced subgraph of . Moreover, for a vertex , we write and for a subset we write .
For a set of graphs, a graph is -free if has no induced subgraph isomorphic to a graph in ; if we may write -free instead of -free. The complement of is the graph with vertex set and an edge between two vertices and if and only if .
Recall that the contraction of an edge removes the vertices and from a graph and replaces them by a new vertex that is made adjacent to precisely those vertices that were adjacent to or in . This new graph will be denoted by . In that case we may also say that is contracted onto , and we use to denote the new vertex resulting from the edge contraction. The subdivision of an edge removes the edge from and replaces it by a new vertex and two edges and .
Let and be two vertex-disjoint graphs. The join operation adds an edge between every vertex of and every vertex of . The union operation takes the disjoint union of and , that is, . We denote the disjoint union of copies of by . For , the graph denotes the path on vertices, that is, and . For , the graph denotes the cycle on vertices, that is, and . The graph is also called the triangle. The claw is the 4-vertex star, that is, the graph with vertices , , , and edges , , .
Let be a graph. A subset is called a clique of if any two vertices in are adjacent to each other. The clique number is the number of vertices in a maximum clique of . A subset is called an independent set of if any two vertices in are non-adjacent to each other. The independence number is the number of vertices in a maximum independent set of . For a positive integer , a -colouring of is a mapping such that whenever . The chromatic number is the smallest integer for which has a -colouring. A subset of edges is called a matching if no two edges of share a common end-vertex. The matching number is the number of edges in a maximum matching of a graph . A vertex such that contains an edge incident with is saturated by ; otherwise is unsaturated by . A subset is a vertex cover of if every edge of is incident with at least one vertex of .
The Coloring problem is that of testing if a graph has a -colouring for some given integer . The problems Clique and Independent Set are those of testing if a graph has a clique or independent set, respectively, of size at least . The Vertex Cover problem is that of testing if a graph has a vertex cover of size at most . We need the following lemma at several places in our paper.
Lemma 1 ([42])
Vertex Cover* is NP-complete for -free graphs.*
An interval graph is a graph such that one can associate an interval of the real line with every vertex such that two vertices are adjacent if and only if the corresponding intervals intersect. A graph is cobipartite if it is the complement of a bipartite (2-colourable) graph. A graph is chordal if it contains no induced cycle on more than three vertices. A graph is a split graph if it has a split partition, which is a partition of its vertex set into a clique and an independent set . Split graphs coincide with -free graphs [18]. A -free graph is also called a cograph.
A graph is perfect if the chromatic number of every induced subgraph equals the size of a largest clique in that subgraph. A hole is an induced cycle on at least five vertices and an antihole is the complement of a hole. A hole or antihole is odd if it contains an odd number of vertices. We need the following well-known theorem of Chudnovsky, Robertson, Seymour, and Thomas. This theorem can also be used to verify that the other graph classes in Table 1 are indeed subclasses of perfect graphs.
Theorem 2.1 (Strong Perfect Graph Theorem [9])
A graph is perfect if and only if it contains no odd hole and no odd antihole.
3 Cobipartite Graphs, Bipartite Graphs and Trees
We first consider the contraction blocker problems and then the deletion blocker problems.
3.1 Contraction Blockers
Our first result is a hardness result for cobipartite graphs that follows directly from a known result.
Theorem 3.1
-Contraction Blocker()* is NP-complete for cobipartite graphs.*
Proof
Golovach, Heggernes, van ’t Hof and Paul [21] considered the -Club Contraction problem. Recall that this problem takes as input a graph and an integer and asks whether can be -contracted into a graph with diameter at most for some fixed integer . They showed that -Club Contraction is NP-complete even for cobipartite graphs. Graphs of diameter 1 are complete graphs, that is, graphs with independence number 1, whereas cobipartite graphs that are not complete have independence number 2. ∎
We now focus on and . For our next result (Theorem 3.2) we need some additional terminology. A biclique is a complete bipartite graph, which is nontrivial if it has at least one edge. A biclique vertex-partition of a graph is a set of mutually vertex-disjoint bicliques in such that every vertex of is contained in one of the bicliques of . The Biclique Vertex-Partition problem consists in testing whether a given graph has a biclique vertex-partition of size at most , for some positive integer . Fleischner et al. [17] showed that this problem is NP-complete even for bipartite graphs and .
We are now ready to prove Theorem 3.2.
Theorem 3.2
For , Contraction Blocker() is NP-complete for cobipartite graphs.
Proof
Since cobipartite graphs are perfect and closed under edge contractions, we may assume without loss of generality that . The problem is in NP, as Coloring is polynomial-time solvable on cobipartite graphs and then we can take the sequence of edge contractions as certificate. We reduce from Biclique Vertex-Partition. Recall that this problem is NP-complete even for bipartite graphs and [17]. As the problem is polynomial-time solvable for bipartite graphs and (see [17]), we may ask for a biclique vertex-partition of size exactly 3, in which each biclique is nontrivial.
Let () be an instance of Biclique Vertex-Partition, where is a connected bipartite graph on vertices that has partition classes and . We claim that has a biclique vertex-partition consisting of three non-trivial bicliques if and only if can be -contracted into a graph with (so ).
First suppose that has a biclique vertex-partition of size 3. Let be the three (nontrivial) bicliques in . Let be the two bipartition classes of for . So, in , we have that , are six cliques that partition the vertices of , and moreover, there is no edge between a vertex of and a vertex of , for . In we contract each clique to a single vertex that we give colour , and we contract each clique to a single vertex that we give colour as well. In this way we have obtained a 6-vertex graph (so the number of contractions is ) with a 3-colouring. Thus, .
Now suppose that can be -contracted into a graph with . We first observe that the class of cobipartite graphs is closed under taking edge contractions; indeed, if is an edge connecting two vertices of the same partition class, then contracting results in a smaller clique, and if is an edge connecting two vertices of two different partition classes, then contracting is equivalent to removing one of its end-vertices and making its other end-vertex adjacent to every other vertex in the resulting graph.
As the class of cobipartite graphs is closed under taking contractions, is cobipartite. As cobipartite graphs have independence number at most 2, each colour class in a colouring of must have size at most 2. Consequently, must have exactly six vertices , such that form a clique, form a clique, and moreover, and are not adjacent, for . This means that we did not contract an edge with and (as the resulting vertex would be adjacent to all other vertices). Hence, we may assume without loss of generality that for , each corresponds to a set of vertices (that we contracted into the single vertex ) and that each corresponds to a set of vertices (that we contracted into the single vertex ). As each pair is non-adjacent, no vertex of is adjacent to a vertex of . Consequently, in , we find that each set induces a biclique. Hence, the sets , and form a biclique vertex-partition of that has size 3.∎
We now assume that is fixed. We show that -Contraction Blocker() becomes polynomial-time solvable on cobipartite graphs for . For , we can prove this even for the class of graphs with independence number at most 2, or equivalently, the class of -free graphs, which properly contains the class of cobipartite graphs.
Theorem 3.3
For any fixed , the -Contraction Blocker() problem can be solved in polynomial time for -free graphs.
Proof
Let be a graph with . Consider a colouring with colours. The size of every colour class is at most . Hence every subgraph of induced by two colour classes has at most vertices, and as such has a spanning forest with in total at most edges. This means that we can contract two colour classes to an independent set (that is, to a new colour class) by using at most contractions. This observation gives us the following algorithm. We guess a set of at most contractions. Afterward we decrease by 1 and repeat this procedure until . For each resulting graph we check whether . If so, then the algorithm returns a yes-answer and otherwise a no-answer.
Let be the number of edges of . Then the total number of guesses is at most , which is polynomial as is fixed. Because Coloring is polynomial-time solvable on graphs with independence number at most 2 and this class is closed under edge contractions, our algorithm runs in polynomial time. ∎
Corollary 1
Let . For any fixed , the -Contraction Blocker() problem can be solved in polynomial time on cobipartite graphs.
Proof
For this follows immediately from Theorem 3.3. As cobipartite graphs are perfect and closed under edge contraction, we obtain the same result for .∎
We now consider the class of bipartite graphs. If , then Contraction Blocker() is trivial for bipartite graphs (and thus also for trees). To the contrary, for , we will show that Contraction Blocker() is NP-hard for bipartite graphs. The complexity of -Contraction Blocker() remains open for bipartite graphs. Bipartite graphs are not closed under edge contraction. Therefore membership to NP cannot be established by taking a sequence of edge contractions as the certificate, even though due to König’s Theorem (see, for example, [14]), Independent Set is polynomial-time solvable for bipartite graphs.
Theorem 3.4
Contraction Blocker()* is NP-hard on bipartite graphs.*
Proof
We know from Theorem 3.1 that -Contraction Blocker() is NP-complete on cobipartite graphs. Consider a cobipartite graph with edges and an integer , which together form an instance of -Contraction Blocker(). Subdivide each of the edges of in order to obtain a bipartite graph . We claim that is a yes-instance of 1-Contraction Blocker() if and only if is a yes-instance of Contraction Blocker().
First suppose that is a yes-instance of 1-Contraction Blocker(). In we first perform edge contractions to get back. We then perform edge contractions to get independence number . Hence, is a yes-instance of Contraction Blocker().
Now suppose that is a yes-instance of Contraction Blocker(). Then there exists a sequence of edge contractions that transform into a complete graph . We may assume that has size at least 4 (as we could have added without loss of generality three dominating vertices to without increasing ). As has size at least 4, each subdivided edge must be contracted back to the original edge again. This operation costs edge contractions, so we contract to using at most edge operations. Hence, is a yes-instance of 1-Contraction Blocker(). This proves the claim and hence the theorem.∎
We complement Theorem 3.4 by showing that Contraction Blocker() is linear-time solvable on trees. In order to prove this result we make a connection to the matching number of a graph.
Theorem 3.5
Contraction Blocker()* is linear-time solvable on trees.*
Proof
Let be an instance of Contraction Blocker(), where is a tree on vertices. We first describe our algorithm and prove its correctness. Afterwards, we analyze its running time. Throughout the proof let denote a maximum matching of .
As by König’s Theorem (see, for example, [14]), we find that is a no-instance if . Assume that . We observe that trees are closed under edge contraction. Hence, contracting an edge of results in a new tree . Moreover, has vertices and the edge contraction neither increased the independence number nor the matching number. As and similarly , this means that either or .
First suppose that . There are exactly vertices that are unsaturated by . Let be an edge, such that is unsaturated. As is maximum, must be saturated. Then, by contracting , we obtain a tree such that . It follows from the above that . Say that we contracted onto . Then in we have that is saturated by , which is a maximum matching of as well. Thus, if , contracting edges, one of the end-vertices of which is unsaturated by , yields a tree with and . Since an edge contraction reduces the independence number by at most 1, it follows that this is optimal. Hence, as , we find that is a yes-instance if and a no-instance if .
Now suppose that . Suppose that we first contract the edges that have exactly one end-vertex that is unsaturated by . It follows from the above that this yields a tree with and . Since does not contain any unsaturated vertex, is a perfect matching of . Then, contracting any edge in results in a tree with and thus, . If we contract an edge , the resulting vertex is unsaturated by in . Hence, as explained above, if in addition we contract now an edge , we obtain a tree with and . Repeating this procedure, we may reduce the independence number of by with edge contractions. Below we show that this is optimal.
Suppose that we contract edges in . Let be the resulting tree. We have . As , this means that . If we have , and thus
[TABLE]
So at least edge contractions are necessary to decrease the independence number by . It remains to check if is sufficiently high for us to allow this number of edge contractions.
As we can find a maximum matching of tree (and thus compute ) in time by using the algorithm of Savage [44], our algorithm runs in time. ∎
Remark 1. By König’s Theorem, we have that for any bipartite graph , but we can only use the proof of Theorem 3.5 to obtain a result for trees for the following reason: trees form the largest subclass of (connected) bipartite graphs that are closed under edge contraction, and this property plays a crucial role in our proof.
3.2 Deletion Blockers
We first show that all three deletion blocker problems are polynomial-time solvable for bipartite graphs (and thus for trees). It is known already that Deletion Blocker() is polynomial-time solvable for bipartite graphs [3, 13]. Hence it suffices to prove that the same holds for Deletion Blocker() when . In order to do so we need the following relation between -Deletion Blocker() and Vertex Cover.
Proposition 1
Let be a graph with at least one edge and let be an integer. Then is a yes-instance of Deletion Blocker() if and only if is a yes-instance of Vertex Cover.
Proof
Let be a graph with . Thus, . Let be an integer. First suppose that is a yes-instance of Vertex Cover, that is, has a vertex cover of size at most . So, every edge of is incident to at least one vertex of . Then, deleting all vertices of yields a graph with no edges. This means that , and thus is a yes-instance for Deletion Blocker(). Now suppose that is a yes-instance of Deletion Blocker(). Then there exists a set of size such that . This implies that has no edges. Thus is a vertex cover of of size at most . So, is a yes-instance for Vertex Cover. ∎
Proposition 1 has the following corollary, which we will apply in this section and at some other places in our paper.
Corollary 2
Let be a triangle-free graph with at least one edge and let be an integer. Then is a yes-instance of 1-Deletion Blocker() if and only if is a yes-instance of Vertex Cover.
We are now ready to prove the following result.
Theorem 3.6
For , Deletion Blocker() can be solved in polynomial time on bipartite graphs.
Proof
As bipartite graphs are perfect and closed under vertex deletion, the problems Deletion Blocker() and Deletion Blocker() are equivalent. Therefore, we only have to consider the case where . As bipartite graphs have clique number at most 2, Deletion Blocker() and -Deletion Blocker() are equivalent. As bipartite graphs are triangle-free, we can apply Corollary 2. To solve Vertex Cover on bipartite graphs, König’s Theorem tells us that it suffices to find a maximum matching, which takes time on -vertex bipartite graphs [27].∎
We now consider the the class of cobipartite graphs. It is known that Deletion Blocker() is polynomial-time solvable on cobipartite graphs if [13]. Hence we only have to deal with the case . For this case we prove the following result, which follows immediately from Theorem 3.6.
Theorem 3.7
Deletion Blocker()* can be solved in polynomial time on cobipartite graphs.*
4 Cographs
It is well known (see for example [8]) that a graph is a cograph if and only if can be generated from by a sequence of operations, where each operation is either a join or a union operation. Recall from Section 2 that we denote these operations by and , respectively. Such a sequence corresponds to a decomposition tree , which has the following properties:
its root corresponds to the graph ;
- 2.
every leaf of corresponds to exactly one vertex of , and vice versa, implying that corresponds to a unique single-vertex graph ;
- 3.
every internal node of has at least two children, is either labeled or , and corresponds to an induced subgraph of defined as follows:
- –
if is a -node, then is the disjoint union of all graphs where is a child of ;
- –
if is a -node, then is the join of all graphs where is a child of .
A cograph may have more than one such tree but has exactly one unique tree [11], called the cotree of , if the following additional property is required:
Labels of internal nodes on the (unique) path from any leaf to alternate between and .
Note that has vertices. For our purposes we must modify by applying the following known procedure (see for example [6]). Whenever an internal node of has more than two children and , we remove the edges and and add a new vertex with edges , and . If is a -node, then is a -node, and if is a -node, then is a -node. Applying this rule exhaustively yields a tree in which each internal node has exactly two children. We denote this tree by . Because has vertices, modifying into takes linear time.
Corneil, Perl and Stewart [12] proved that the problem of deciding whether a graph with vertices and edges is a cograph can be solved in time . They also showed that in the same time it is possible to construct its cotree (if it exists). As modifying into takes time, we obtain the following lemma.
Lemma 2
Let be a graph with vertices and edges. Deciding if is a cograph and constructing (if it exists) can be done in time .
For two integers and we say that a graph can be -contracted into a graph if can be modified into by a sequence containing edge contractions and vertex deletions. Note that cographs are closed under edge contraction and under vertex deletion. In fact, to prove our results for cographs, we will prove the following more general result.
Theorem 4.1
Let . The problem of determining the largest integer such that a cograph with vertices and edges can be -contracted into a cograph with can be solved in time.
Proof
First consider . Let be a cograph with vertices and edges and let be two positive integers. We first construct . We then consider each node of by following a bottom-up approach starting at the leaves of and ending in its root .
Let be a node of . Recall that is the subgraph of induced by all vertices that corresponds to leaves in the subtree of rooted at . With node we associate a table that records the following data: for each pair of integers with we compute the largest integer such that can be -contracted into a graph with . We denote this integer by . Let with .
Case 1. is a leaf.
Then is a 1-vertex graph meaning that if , whereas if .
Case 2. is a -node.
Let and be the two children of . Then, as is the disjoint union of and , we find that . Hence, we have
[TABLE]
Case 3. is a -node.
Since is a -node, is connected and as such has a spanning tree . If and , then we can contract edges of in the graph followed by vertex deletions. As each operation will reduce by exactly one vertex, this results in the empty graph. Hence, . From now on assume that or . As such, any graph we can obtain from by using edge contractions and vertex deletions is non-empty and hence has independence number at least 1.
Let and be the two children of . Then, as is the join of and , we find that . In order to determine we must do some further analysis. Let be a sequence that consists of edge contractions and vertex deletions of such that applying on results in a graph with . We partition into five sets , , , , , respectively, as follows. Let and be the set of contractions of edges with both end-vertices in and with both end-vertices in , respectively. Let be the set of contractions of edges with one end-vertex in and the other one in . Let and let . Then . Let and be the set of deletions of vertices in and , respectively. Let . Then . We distinguish between two cases.
First assume that . Then . Let be the graph obtained from after applying the subsequence of , consisting of operations in , on . Let be defined analogously. Then we have
[TABLE]
where the second equality follows from the definition of .
Now assume that . Recall that or . Hence . Our approach is based on the following observations.
First, contracting an edge with one end-vertex in and the other one in is equivalent to removing these two end-vertices and introducing a new vertex that is adjacent to all other vertices of (such a vertex is said to be universal).
Second, assume that contains two distinct vertices and and that contains two distinct vertices and . Now suppose that we are to contract two edges from . Contracting two edges of this set that have a common end-vertex, say edges and , is equivalent to deleting from and introducing a new universal vertex. Contracting two edges with no common end-vertex, say and , is equivalent to deleting all four vertices from and introducing two new universal vertices. Because the two new universal vertices in the latter choice are adjacent, whereas the vertex may not be universal after making the former choice, the latter choice decreases the independence number by the same or a larger value than the former choice. Hence, we may assume without loss of generality that the latter choice happened. More generally, the contracted edges with one end-vertex in and the other one in can be assumed to form a matching. We also note that introducing a new universal vertex to a graph does not introduce any new independent set other than the singleton set containing the vertex itself.
We conclude that each edge contraction in may be considered to be equivalent to deleting one vertex from and one from and introducing a new universal vertex. If one of the two graphs or becomes empty in this way, then an edge contraction in can be considered to be equivalent to the deletion of a vertex of the other one. Finally, if both sets and become empty, then we can stop as in that case has independence number 1 (which we assumed was the smallest value of ).
By the above observations and the definition of we find that
[TABLE]
Hence we can do as follows. We consider all tuples with and and compute . Let be the minimum value over all values found. We then consider all tuples with , , and and compute . Let be the minimum value over all values found. Then .
After reaching the root , we let our algorithm return the integer . By construction, is the largest integer such that can be -contracted into a graph with . We are left to analyze the running time.
Constructing can be done in time by Lemma 2. We now determine the time it takes to compute one entry in the table associated with a node . It takes linear time to compute the independence number of a cograph111For a cograph , compute and use the formula if is a -node with children and and otherwise. Alternatively, see for example [10] for a linear-time algorithm on a superclass of cographs.. The total number of tuples and that we need to consider is . Note that the table associated with a node has entries but that we only have to compute once. Hence, it takes time to construct a table for a node. As has vertices, the total running time is .
Now consider . Note that we cannot consider the complement of a cograph (which is a cograph) because an edge contraction in a graph does not correspond to an edge contraction in its complement. However, we can re-use the previous proof after making a few modifications. Let be a cograph with vertices and edges and let be two positive integers. We follow the same approach as in the proof for . We only have to swap Cases 2 and 3 after observing that if is a -node with and as its two children and if is a -node. We can use the same arguments as used in the proof for for the running time analysis as well; we only have to observe that it takes time to compute the chromatic number of a cograph (using the same arguments as before or by using another algorithm of [10]).
Finally consider . As cographs are perfect and closed under edge contractions, the proof follows immediately from the corresponding result for .∎
Corollary 3
For , both the Contraction Blocker() problem and the Deletion Blocker() problem can be solved in polynomial time for cographs.
Proof
We use Theorem 4.1 after setting for Contraction Blocker() and for Deletion Blocker().∎
5 Split Graphs
A split partition of a split graph is minimal if is not an independent set for all , in other words every vertex is adjacent to some vertex . Note that for a minimal split partition we have . A split partition is maximal if is not a clique for all , in other words every vertex is non adjacent to at least one vertex . Note that for a maximal split partition we have . We first show the following result.
Theorem 5.1
Let . For any fixed , the -Contraction Blocker problem is polynomial-time solvable on split graphs.
Proof
First consider . Let be an instance of -Contraction Blocker where is a split graph. Let be a minimal split partition of . Let be the set of vertices in that have at least one neighbour in , and let . Because is a split graph, all vertices of belong to the same connected component of . Moreover, we have .
First suppose that . For to be a yes-instance, must be contracted into a graph with . This means that we must contract into the empty graph, which is not possible. Hence, is a no-instance in this case. Hence, we may assume without loss of generality that .
Suppose that . If , then we contract every vertex of onto a neighbour in . In this way we have -contracted into a graph with . So, is a yes-instance in this case. If , we contract each vertex of an arbitrary subset of vertices of onto a neighbour in . In this way we have -contracted into a graph with . So, is a yes-instance in this case as well.
If , then we consider all possible sequences of at most edge contractions. This takes time , which is polynomial as , and consequently , is fixed. For every such sequence we check in polynomial time whether the resulting graph has stability number at most . As split graphs are closed under edge contraction and moreover are chordal graphs, the latter can be verified in linear time (see [22]).
Now let . Let be an instance of -Contraction Blocker where is a split graph.
Case 1. .
For to be a yes-instance, must be -contracted into a graph with . The only graph with chromatic number at most [math], is the empty graph. However, a non-empty graph cannot be contracted to an empty graph. Hence, is a no-instance in this case.
Case 2. .
For to be a yes-instance, must be -contracted into a graph with . Hence, every connected component of must consist of exactly one vertex. If has no connected components with edges, then is a yes-instance. Otherwise, because is a split graph, has exactly one connected component containing one or more edges. In that case, is a yes-instance if and only if ; this can be checked in constant time.
Case 3. .
First, assume that . Because every edge contraction reduces the chromatic number by at most 1, is a no-instance.
Second, assume that . We consider all possible sequences of at most edge contractions. This takes time , which is polynomial as , and consequently , is fixed. For every such sequence we check in polynomial time whether the resulting graph has chromatic number at most . As split graphs are closed under edge contractions and moreover are chordal graphs, the latter can be verified in polynomial time (see [22]).
Third, assume that . We claim that is a yes-instance. This can be seen as follows. Let be a maximal split partition of .
If , then we contract arbitrary edges of . The resulting graph has a split partition with . Hence . Note that the latter equality follows from our assumption that is maximal. Now suppose that . We contract arbitrary edges of . The resulting graph has chromatic number . Hence, in both cases, we conclude that is a yes-instance.
Finally consider . We use the previous result combined with the fact that split graphs are perfect and closed under edge contractions. ∎
In our next theorem we give two hardness results which, as explained in Section 1, show that Theorem 5.1 can be seen as best possible. In their proofs we will reduce from the Red-Blue Dominating Set problem. This problem takes as input a bipartite graph and an integer , and asks whether there exists a red-blue dominating set of size at most , that is, a subset of at most vertices such that every vertex in has at least one neighbour in . This problem is NP-complete, because it is equivalent to the NP-complete problems Set Cover and Hitting Set [20]. The Red-Blue Dominating Set problem is also -complete when parameterized by [24]. Belmonte et al. [5] reduced from the same problem for showing that -Contraction Blocker) is NP-complete and W[2]-hard (with parameter ) for split graphs, but the arguments we use to prove our results are quite different from the ones they used.
Theorem 5.2
For , the Contraction Blocker problem, restricted to split graphs, is NP-complete as well as -hard when parameterized by .
Proof
The problem is in NP for , as split graphs are closed under edge contraction and the three problems Clique Coloring and Independent Set are readily seen to be polynomial-time solvable on split graphs; hence, we can take the sequence of edge contractions as the certificate. Recall that we reduce from Red Blue Dominating Set in order to show NP-hardness and W[1]-hardness with parameter .
First consider . Let be a bipartite graph that together with an integer forms an instance of Red-Blue Dominating Set. We may assume without loss of generality that . Moreover, we may assume that every vertex of is adjacent to at least one vertex of . We add all possible edges between vertices in . This yields a split graph with a split partition . Because every vertex in is assumed to be adjacent to at least one vertex of in , we find that is a minimal split partition of .
Because Red-Blue Dominating Set problem is NP-complete [20] and W[1]-complete when parameterized by [24], it suffices to prove that has a red-blue dominating set of size at most if and only if is a yes-instance of -Contraction Blocker(). We prove this claim below.
First suppose that has a red-blue dominating set of size at most . Because , we may assume without loss of generality that (otherwise we would just add some vertices from to ).
In we contract every onto a neighbour in . In this way we -contracted into a graph . Note that is a split graph that has a split partition . Because every vertex in is adjacent to at least one vertex of in by definition of , it is adjacent to at least one vertex of in . The latter statement is still true for , as contracting an edge incident to a vertex is equivalent to deleting . Hence, is a minimal split partition of , so . Because is a minimal split partition of , we have . This means that . We conclude that is a yes-instance of -Contraction Blocker().
Now suppose that is a yes-instance of -Blocker(), that is, can be -contracted into a graph such that . Recall that . Hence, . Let be the number of contractions of edges with one end-vertex in . Note that any such contraction decreases the size of the independent set by exactly one. If , then contains an independent set of size , which would mean that , a contradiction. Hence, , which implies that as we performed no more than contractions in total. Let denote the independent set obtained from after all edge contractions. Then we find that . Hence, , which means that is a minimal split partition of . This means that every vertex of is adjacent to at least one vertex of in . Because all our contractions were performed on edges with one end-vertex in , we have only removed vertices from , that is, is an induced subgraph of . Hence, every vertex of is adjacent to at least one vertex of in . Consequently, is a red-blue dominating set of with size .
Now consider . Let be a bipartite graph that together with an integer forms an instance of Red-Blue Dominating Set. We may assume without loss of generality that . Moreover, we may assume that every vertex of is adjacent to at least one vertex of .
We take the bipartite complement of , that is, we construct the bipartite graph with partition classes and , and we add an edge between any two vertices and if and only if . Then, we add all possible edges between vertices in . Finally we add a new vertex to the graph. We make adjacent to all vertices of . This yields a split graph with a split partition . Because every vertex in is assumed to be adjacent to at least one vertex of in , it is non-adjacent to at least one vertex of in . Hence, is a maximal split partition of (we will explain the role of vertex in our construction later). Similarly to the previous case, we claim that has a red-blue dominating set of size at most if and only if is a yes-instance of -Contraction Blocker(). We prove this claim below.
First suppose that has a red-blue dominating set of size at most . Because , we may assume without loss of generality that (otherwise we would just add some vertices from to ).
In we contract every onto . In this way we -contracted into a graph . Note that is a split graph that has a split partition . Because every vertex in is adjacent to at least one vertex of in by definition of , it is non-adjacent to at least one vertex of in . The latter statement is still true for , as no vertex of was involved in any of the edge contractions performed. Hence, is a maximal split partition of , so . Because is a maximal split partition of , we have . This means that . We conclude that is a yes-instance of -Contraction Blocker().
Now suppose that is a yes-instance of -Blocker(), that is, can be -contracted to a graph such that . Recall that . Hence, . Let be the number of contractions of edges between two vertices of . Note that any such contraction decreases the size of the clique by exactly one. If , then contains a clique of size , which would mean that , a contradiction. Hence, , which implies that as we performed no more than contractions in total. Let denote the clique obtained from after all edge contractions. Then we find that
[TABLE]
Hence, , which means that is a maximal split partition of . This means that no vertex of is adjacent to all vertices of in .
We may assume without loss of generality that , as we can view any edge contraction of an edge between a vertex and as a contraction of onto . Furthermore, suppose we performed a contraction of an edge with , say we contracted onto . We change this by contracting onto instead. Because is adjacent to all vertices of in , we find that is adjacent to all vertices (except to itself) of and of any intermediate graph that we obtained while contracting into . Hence, contracting onto is equivalent to deleting . As such, contracting onto does not lead to a vertex becoming adjacent to all vertices of . Consequently, the size of a maximum clique in the modified graph is also equal to . As we can do the same for any other contraction of an edge between two vertices in , we may assume without loss of generality that every edge contraction is a contraction of a vertex of onto .
Let . As noted, contracting a vertex of onto is the same as deleting such a vertex of from the graph. Hence, every vertex of has exactly the same neighbours in as it has in . Because every vertex in is adjacent to but not to all vertices of , we find that every vertex in is non-adjacent to at least one vertex of in , and consequently, in . Because and , we find that . We conclude that is a red-blue dominating set of with size .
Finally, consider . As split graphs are perfect and closed under edge contractions, this case follows directly from the previous case where .∎
Regarding the Deletion Blocker problem, for , we know from [13] that it is NP-complete. In the same paper it was shown that if is fixed, all three problems become polynomially solvable.
6 Interval Graphs
Let be an interval graph with vertices and edges that corresponds to a set of intervals on the real line. Let be such that vertex corresponds to interval for . Note that the class of interval graphs is closed under edge contraction. Indeed, contracting an edge corresponds to removing the intervals and and adding a new interval . It is well known (see e.g. [19]) that has at most maximal cliques which can be linearly ordered in time so that the maximal cliques containing a vertex appear consecutively for .
We first prove a useful lemma for the class of -free graphs, which contains the class of interval graphs as a proper subclass.
Lemma 3
Let be a -free graph and let . Let be the graph obtained after the contraction of and let be the new vertex replacing and . Then every maximal clique in containing corresponds to a maximal clique in and vice versa, such that
- (a)
either and ; 2. (b)
or and ; 3. (c)
or and .
Moreover, every other maximal clique in is a maximal clique in and vice versa.
Proof
Let (resp. ) be the set of neighbours of (resp. ) that are nonadjacent to (resp. ). Let be the set of vertices adjacent to both and . Now consider a clique in containing . As is -free, we find that , and hence , contains no edge between a vertex in and a vertex in . Therefore we are in exactly one of the following cases:
- (i)
contains one or more vertices from both and but no vertices from ;
- (ii)
contains one or more vertices from both and but no vertices from ;
- (iii)
contains one or more vertices from but no vertices from and ;
- (iv)
contains one or more vertices from but no vertices from and ;
- (v)
contains one or more vertices from but no vertices from and .
Suppose we are in case (i). Since is maximal, it follows that is a maximal clique in and thus outcome (a) holds. By symmetry, if we are in case (ii), outcome (b) holds. Assume now that case (iii) occurs. Since is maximal, it follows that is a maximal clique in and thus outcome (a) holds. By symmetry, we conclude that if case (iv) occurs, outcome (b) holds. Finally, suppose that we are in case (v). Then is a maximal clique in and thus outcome (c) holds. ∎
Lemma 3 tells us that if we contract an edge in a -free graph, every maximal clique containing both end-vertices of will have its size reduced by exactly one in the resulting graph, and moreover, the size of every other maximal clique of the original graph will remain the same and we do not create any new maximal clique.
Lemma 4
Let be an interval graph and let be an integer. Let be the first maximal clique of size strictly greater than starting left on the real line, and let be the intervals with the rightmost right endpoints among all intervals corresponding to the vertices in . Let be a set of edges such that the graph obtained from after having contracted all edges from satisfies . Then there exists a set such that , where and such that the graph obtained from after contracting all edges in satisfies .
Proof
We first note that, by their definition, and are contained in all maximal cliques of size strictly greater than that contain at least two vertices of . Moreover, contracting the edge instead of another edge of does not create cliques of larger size, due to Lemma 3. ∎
Lemma 4 tells us that if for an interval graph the answer of the Contraction Blocker() problem is yes, then there always exists a set with such that , where is the graph obtained from by contracting the edges of , and where belong to the first maximal clique in with size strictly greater than starting left on the real line and such that have the rightmost right endpoints among all intervals corresponding to vertices in . Since interval graphs are closed under edge contractions, we can use this property recursively to obtain a polynomial-time algorithm for Contraction Blocker(), with , in interval graphs.
Theorem 6.1
Let . Then Contraction Blocker() can be solved in polynomial time on interval graphs.
Proof
Since interval graphs are perfect and closed under edge contractions, we may assume without loss of generality that . Let be an interval graph and let be an integer. Our algorithm goes as follows. Let be the first maximal clique of size strictly greater than starting left on the real line. By Lemma 4, we know that if there exists a solution, then there exists one in which we contract the edge where are such that the corresponding intervals have the rightmost right endpoints among all intervals corresponding to vertices in . So we contract the edge . Since the resulting graph is still an interval graph, we may repeat our procedure. We consider again the first maximal clique of size strictly greater than starting left on the real line and contract the edge whose end-vertices correspond to the intervals with the rightmost right endpoints among all intervals corresponding to vertices in that clique. We continue like this until there is no more maximal clique of size strictly greater than in the graph.
The correctness of our algorithm follows from Lemmas 3 and 4. Indeed, by Lemma 3 we know that our choice of the edges that we contract is such that at each step there is at least one maximal clique of size strictly greater than whose size is reduced by one and furthermore, we do not create any new maximal clique. Since an interval graph on vertices contains at most maximal cliques, it follows that our algorithms stop after at most steps. Since all maximal cliques of an interval graph can be found in time , where is the number of edges, we then find that our algorithm runs in time . Finally, Lemma 4 ensures that the set of edges we choose to contract has minimum size. ∎
The proof of Theorem 6.1 can be readily adapted to show polynomial-time solvability of the Deletion Blocker() problem on interval graphs for .
Theorem 6.2
Let . Then Deletion Blocker() can be solved in polynomial time on interval graphs.
We recall that for the complexity of both problems is open for interval graphs.
7 Chordal Graphs
The following result shows that Theorem 6.1 cannot be generalized to chordal graphs.
Theorem 7.1
For , -Contraction Blocker() is NP-complete for chordal graphs.
Proof
Since chordal graphs are perfect and closed under taking edge contractions, we may assume without loss of generality that . As Clique is polynomial-time solvable on chordal graphs, this means that the problem is in NP (take the sequence of edge contractions as the certificate). We reduce from Vertex Cover, which is well known to be NP-complete (see [20]).
Let be a graph that together with an integer forms an instance of Vertex Cover. From we construct a chordal graph as follows. We introduce a new vertex not in . We represent each edge of by a clique in of size so that whenever . We represent each vertex of by a vertex in that we also denote by . Then we let the vertex set of be . We add an edge between every vertex in and a vertex if and only if is incident with in . In we let the vertices of form a clique. Finally, we add all edges between and any vertex in . Note that the resulting graph is indeed chordal. Note also that (every maximum clique consists of , the vertices of a clique and their two neighbours in ).
We claim that has a vertex cover of size at most if and only if can be -contracted to a graph with . First suppose that has a vertex cover of size at most . For each vertex , we contract the corresponding vertex in to . As , this means that we -contracted into a graph . Since is a vertex cover, we obtain .
Now suppose that can be -contracted to a graph with . Let be a corresponding sequence of edge contractions (so holds). By Lemma 3 and the fact that chordal graphs are closed under taking edge contractions, we find that no contraction in results in a new maximum clique. Hence, as we need to reduce the size of each maximum clique by at least 1, we may assume without loss of generality that each contraction in concerns an edge with both its end-vertices in . We construct a set as follows. If contains the contraction of an edge we select . If contains the contraction of an edge , we select one of arbitrarily. Because each maximum clique must be reduced, we find that is a vertex cover. By construction, . This completes the proof.∎
Similar arguments as in the above proof can be readily used to prove the following result, which shows that Theorem 6.2 cannot be generalized to chordal graphs.
Theorem 7.2
For , -Deletion Blocker() is NP-complete for chordal graphs.
8 Six Dichotomy Results and -free Perfect Graphs with
In this section we first prove that for the contraction and deletion blocker problems become very quickly NP-hard when we increase , that is, we prove Theorem 1.1.
Theorem 1.1 (restated). The following six dichotomies hold:
- (i)
Contraction Blocker()* is polynomial-time solvable for graphs with and 1-Contraction Blocker() is NP-complete for graphs with ;*
**
- (ii)
Contraction Blocker()* is polynomial-time solvable for graphs with and -Contraction Blocker() is NP-complete for graphs with ;*
**
- (iii)
Contraction Blocker()* is polynomial-time solvable for graphs with and -Contraction Blocker() is NP-complete for graphs with ;*
**
- (iv)
Deletion Blocker()* is polynomial-time solvable for graphs with and*
-Deletion Blocker()* is NP-complete for graphs with ;*
**
- (v)
Deletion Blocker()* is polynomial-time solvable for graphs with and*
-Deletion Blocker()* is NP-complete for graphs with ;*
**
- (vi)
Deletion Blocker()* is polynomial-time solvable for graphs with and*
-Deletion Blocker()* is NP-complete for graphs with ;*
Proof
All six problems are readily seen to be in NP for the above graph classes (it suffices to take the sequence of edge contractions or vertex deletions as a certificate). We prove each of the six statements separately.
(i) The problem is trivial if . As cobipartite graphs have independence number at most 2, we can apply Theorem 3.1 to obtain NP-completeness if .
(ii) The problem is trivial if . We now consider the class of graphs with . Recall that the problem Bipartite Contraction is to test whether a graph can be made bipartite by at most edge contractions. It is readily seen that -Contraction Blocker() and Bipartite Contraction are equivalent for graphs of chromatic number 3. Heggernes, van ’t Hof, Lokshtanov and Paul [26] observed that Bipartite Contraction is NP-complete by reducing from the NP-complete problem Edge Bipartization, which is that of testing whether a graph can be made bipartite by deleting at most edges. Given an instance of Edge Bipartization, they obtain an instance of Bipartite Contraction by replacing every edge in by a path of sufficiently large odd length. Note that the resulting graph has chromatic number 3 (assign colour 1 to the vertices of and give the new vertices colours 2 and 3).
(iii) The problem is trivial if . We now consider the class of graphs with . We use a polynomial reduction from the problem ONE-IN-3-SAT, which is well known to be NP-complete (see [20]). This problem has as input a set of boolean variables and a collection of clauses over such that for . The question is whether there a truth assignment for such that each clause of contains exactly one true literal.
Let be an instance of ONE-IN-3-SAT. We construct an instance of -Contraction Blocker(), where is constructed as follows (see Fig. 1 for an example):
- •
For each variable , introduce five vertices forming a triangle and a square sharing exactly one edge. This yields the gadget for the variable , where the two edges that do not belong to the square correspond to the two literals and .
- •
For each clause , introduce three vertices forming a triangle . This yields the gadget for the clause , where each edge corresponds to one of the three literals forming .
- •
For every edge of a triangle corresponding to a literal , link its two end-vertices by a matching to the two end-vertices of the edge corresponding to in the variable gadget.
Observe that can be obtained in polynomial time. Moreover, and contains exactly disjoint triangles. Thus, in order to obtain a graph from with , we need to contract at least one edge from each of these triangles. We claim that is a yes-instance of ONE-IN-3-SAT if and only if is a yes-instance of 1-Contraction Blocker().
First suppose that is a yes-instance. For each variable which is true (resp. false), we contract the edge corresponding to the literal (resp. the literal ) in the triangle of the variable gadget; for each clause , we contract the unique edge of the clause gadget corresponding to the literal which is set to true (see Fig. 2). Thus we contract exactly edges, one in each of the disjoint triangles. For each clause gadget in , the unique contracted edge is linked to the unique contracted edge in the variable gadget corresponding to the true literal. Hence the four original vertices are transformed into two adjacent vertices.
We claim that no new triangles are created by performing the edge contractions. Indeed, when contracting an edge from a clause gadget, we do create a triangle one edge of which belongs to a variable gadget. But by construction, this edge will necessarily be contracted as well. Thus this triangle is transformed into a single edge. Hence , which means that is a yes-instance.
Suppose now that is a yes-instance. This means that we can obtain a graph with by contracting edges of . Since contains exactly disjoint triangles, we must, as already mentioned before, contract exactly one edge in each of these triangles. Furthermore, in a variable gadget we must contract an edge not belonging to the square, as otherwise a new triangle is created and hence we would need more than contractions, a contradiction. Let be an edge in a variable gadget that is contracted. Suppose that corresponds to a literal . In , is contained in some squares containing edges of clause gadgets which correspond to . Thus, after this contraction, we create new triangles each containing an edge of a clause gadget corresponding to . It follows that we must contract the edges in the clause gadgets corresponding to the literal , otherwise triangles will remain in . Since we use edge contractions, exactly one edge in each clause gadget is contracted. Hence, by assigning the value true to the literal corresponding to the edge contracted in each variable gadget, one literal has value true and the other two have value false in each clause. This yields a positive answer for , so is a yes-instance.
(iv) & (vi) Both problems are trivial if has value 1. Now consider the class of graphs with , or equivalently the class of triangle-free graphs. Since Vertex Cover is NP-complete for triangle-free graphs by Lemma 1, we conclude from Corollary 2 that -Deletion Blocker() is NP-complete for triangle-free graphs. The remainder of statement (iv) follows immediately after recalling that 1-Deletion Blocker() can be solved by taking the complement of the input graph and solving 1-Deletion Blocker() instead.
(v) First consider the class of graphs with , which coincides with the class of bipartite graphs. Then the problem becomes equivalent to Independent Set, which is polynomial-time solvable for bipartite graphs (due to König’s Theorem; see, for example, [14]). Now consider the class of graphs with . Recall that the Maximum Induced Bipartite Subgraph problem is to test if a given graph contains an induced bipartite subgraph with at least vertices for some integer and that this problem is NP-complete even for the class of 3-colourable perfect graphs [1]. As for 3-colourable graphs -Deletion Blocker() is equivalent to Maximum Induced Bipartite Subgraph, we find that -Deletion Blocker() is NP-complete for graphs with chromatic number .
We have proven each of the six claims and thus have proven the theorem.∎
We note that the graph in the proof of Theorem 1.1 (iii) contains no induced diamond (the complete graph on four vertices minus an edge) and no induced butterfly (the graph with vertices and edges ). As a graph is -free if and only if , we have in fact proven the following.
Corollary 4
The -Contraction Blocker() problem is NP-complete for the class of ()-free graphs.
We use Theorem 1.1 (iii) to prove the following hardness result.
Theorem 8.1
For , -Contraction Blocker() is NP-complete for the class of -free perfect graphs with clique number .
Proof
As before, the problem is readily seen to be in NP. Let , or equivalently, . We adapt the construction used in the proof of Theorem 1.1 (iii) by doing as follows for each edge of the graph in this proof. First we subdivide . This gives us two new edges and . We introduce two new non-adjacent vertices and and make them adjacent to both end-vertices of . Denote the resulting graph by . Note that we got rid of all the induced s while not creating any new induced in this way. Hence is -free. Moreover, we did not introduce any clique on four vertices. Hence, as , we also have . The vertices of the original graph together with the subdivision vertices form a bipartite graph on top of which we placed a number of triangles. Hence, contains no odd hole and no odd antihole. By Theorem 2.1, is perfect.
We increase the allowed number of edge contractions accordingly and observe that, because of the presence of the vertices and for each edge , we are always forced to contract the edge , which gives us back the original construction extended with a number of pendant edges (which do not play a role). Note that we have left the class of -free perfect graphs after contracting away the triangles, but this is allowed. ∎
We recall that Contraction Blocker() is still open for the class of -free perfect graphs as well as Deletion Blocker() for , even if is fixed.
9 -free Graphs
In this section we prove our complexity results for the six blocker problems restricted to -free graphs, that is, we prove Theorem 1.2. To summarize, for we are able to give a dichotomy both for the contraction and deletion blocker problem except for one open case for the contraction blocker problem when . We first consider , then and then .
9.1 When
We call a vertex forced if it is in every maximum independent set of a graph [13]. Recall that the set of all forced vertices is called the core of a graph and that Boros, Golumbic and Levit [7] proved that computing whether the core of a graph has size at least is co-NP-hard for every fixed . As a special case of their result, the problem of testing the existence of a forced vertex is co-NP-hard. We prove that the latter problem, or equivalently, Deletion Blocker() with , stays co-NP-hard even for graphs of girth , or equivalently, -free graphs, for any constant ((the girth of a graph is the length of a shortest cycle in it).
Theorem 9.1
Deletion Blocker()* is co-NP-hard for -free graphs for any constant even if .*
Proof
Let be a graph. We pick one of its edges and subdivide twice, that is, we replace the edge by two new vertices and and edges , , . We let denote the resulting graph. Note that (see also [42]). We claim that has a forced vertex if and only if has a forced vertex.
First suppose that has a forced vertex . Then is also a forced vertex of . In order to see this consider a maximum independent set of . For contradiction, suppose that does not contain . Recall that has size . If is in , then its neighbour is not in , and thus is a maximum independent set of that does not contain , a contradiction. Hence is not in , and for the same reason is not in either. Then is in , as otherwise we could put in to get a larger independent set than . However, we now find that is a maximum independent set of that does not contain , a contradiction. Hence belongs . We conclude that is a forced vertex of as well.
Now suppose that has a forced vertex . First suppose , say . Then is a forced vertex of . In order to see this consider a maximum independent set of . For contradiction, suppose that does not contain . Then is a maximum independent set of not containing , a contradiction. Hence does not belong to , so must be in . Then is also a forced vertex of . In order to see this consider a maximum independent set of . For contradiction, suppose that does not contain . As and are adjacent in , not both of them are in . Assume without loss of generality that is not in . Then is a maximum independent set of that does not contain , a contradiction. We conclude that is a forced vertex of .
We now subdivide each edge of a sufficiently number of times (say times) so that the resulting graph is -free. By repeatedly applying the above claim, we find that has a forced vertex if and only if has a forced vertex. As deciding whether a graph has a forced vertex is co-NP-hard [7], the result follows. ∎
Before we present our two complexity dichotomies for we need one additional observation.
Lemma 5
If is a -free forest, then .
Proof
As is -free, contains at most two connected components. Suppose contains exactly two connected components. Then, as is -free, at least one of these components must be a . As is -free, this means that is an induced subgraph of , so . Suppose is connected. As is -free, contains no claw and no path on more than five vertices. Hence, .∎
We are now ready to present our first dichotomy.
Theorem 9.2
Let be a graph. If , then Deletion Blocker() is polynomial-time solvable for -free graphs, otherwise it is NP-hard or co-NP-hard for -free graphs.
Proof
Let be a graph. If , then we use Corollary 3 to obtain polynomial-time solvability. Suppose is not an induced subgraph of . If contains an induced cycle for some , then we pick and apply Theorem 9.1 to obtain co-NP-hardness even if . Note that for , we could have applied Theorem 5.2 to obtain NP-hardness, as split graphs are -free. Similarly, if , then contains an induced and we could have applied Theorem 5.2 (as split graphs are -free) to obtain NP-hardness as well.
Now assume that is forest. As is not an induced subgraph of , by Lemma 5 either or . If , then we apply Theorem 5.2 again to obtain NP-hardness. If , then we use Theorem 1.1 (iv) to obtain NP-hardness even if , after observing that a graph is -free if and only if . ∎
Remark 2. Recall that -free graphs are closed under vertex deletion. Hence, Deletion Blocker() for -free graphs will be in NP if we can solve Independent Set for -free graphs in polynomial time; in that case we can take a sequence of vertex deletions as certificate. To give an example, Independent Set is polynomial-time solvable for -free graphs [32]. Hence, for -free graphs, Deletion Blocker() is not only NP-hard (which, as argued in the proof of Theorem 9.2, follows from Theorem 5.1) but even NP-complete.
We now consider the edge contraction variant and present our second dichotomy.
Theorem 9.3
Let be a graph. If , then Contraction Blocker() is polynomial-time solvable for -free graphs, otherwise it is NP-hard for -free graphs.
Proof
Let be a graph. If is an induced subgraph of , then we use Corollary 3 to obtain polynomial-time solvability. Now suppose that is not an induced subgraph of . If contains an induced cycle that is odd, then we use Theorem 3.4 to obtain NP-hardness. If contains an induced cycle that is even, then either contains an induced or, if the even cycle has at least six vertices, an induced . This means that we can use Theorem 5.2 to obtain NP-hardness after recalling that split graphs are -free. Assume contains no cycle. Then is a forest. If contains an induced , then we use Theorem 1.1 (i) to obtain NP-hardness even if , after observing that a graph is -free if and only if . Assume is -free. Then by Lemma 5, which means we can use Theorem 5.2 again to obtain NP-hardness. ∎
9.2 When
The complexity dichotomy for Deletion Blocker() follows immediately from Theorem 9.2 after making two observations. First, Deletion Blocker() for -free graphs is equivalent to Deletion Blocker() for -free graphs. Second, the graph is self-complementary, that is, .
Theorem 9.4
Let be a graph. If , then Deletion Blocker() is polynomial-time solvable for -free graphs; otherwise it is co-NP-hard or NP-hard for -free graphs.
We now consider the Contraction Blocker() problem for -free graphs. We start by giving a sufficient condition for computational hardness. Let be a graph class with the following property: if , then so are and for any . We call such a graph class clique-proof.
Theorem 9.5
If Clique is NP-complete for a clique-proof graph class , then Contraction Blocker() is co-NP-hard for , even if .
Proof
Let be a graph class that is clique-proof. From a given graph and given integer we construct the graph . Note that by definition and that . It suffice to prove that if and only if can be -contracted into a graph with .
First suppose that . Then . In we contract an edge of the . This yields the graph , which has clique number , as and . As , this means that .
Now suppose that can be -contracted into a graph with . As contracting an edge in one of the two copies of in does not lower the clique number of , the contracted edge must be in the , that is, . As this did result in a lower clique number, we conclude that and . The latter equality implies that .∎
We need a number of special graphs, namely the cobanner, bull, the aforementioned butterfly and the paw (the graph ), which are all displayed in Figure 3.
We also need the following lemma from Poljak.
Lemma 6 ([42])
The Clique problem is NP-complete for the following classes: -free graphs, -free graphs, cobanner-free graphs and -free graphs.
We use Lemma 6 in the proof of our next lemma.
Lemma 7
Let be a connected graph. If is neither an induced subgraph of nor of the paw, then -Contraction Blocker() is NP-hard or co-NP-hard for -free graphs.
Proof
Let be a connected graph that is neither an induced subgraph of nor of the paw. If contains an induced , use Theorem 8.1. If contains an induced , diamond or butterfly, use Corollary 4. If contains an induced , , , bull or cobanner, use Lemma 6 with Theorem 9.5. So from now on we may assume that is -free. Below we show that this leads to a contradiction.
First suppose that contains no cycle. Then, as is connected, is a tree. Because is -free, is a path. Our assumption that is neither an induced subgraph of nor of the paw implies that contains an induced , which is not possible as is -free.
Now suppose that contains a cycle . Then must have exactly three vertices, because is -free. As is not an induced subgraph of the paw, we find that contains at least one vertex not on . As is connected, we may assume that has a neighbour on . Because is -free, has exactly one neighbour on . Let be this neighbour. Hence, contains an induced paw (consisting of , and the other two vertices of ). As is not an induced subgraph of the paw and is connected, it follows that contains a vertex that is adjacent to a vertex on or to .
Suppose that is adjacent to a vertex of . Then, as is -free, has exactly one neighbour in . If then either contains an induced claw (if and are non-adjacent) or an induced butterfly (if and are adjacent). Since, by our assumption, this is not possible, it follows that . Then, because is bull-free, we deduce that and are adjacent. However, then the vertices, form an induced , which is not possible as is -free. We conclude that is not adjacent to a vertex of , so must be adjacent to only. However, then contains an induced cobanner, a contradiction. This completes the proof of Lemma 7. ∎
A graph is complete multipartite if can be partitioned into independent sets for some integer , such that two vertices are adjacent if and only if they belong to two different sets and . We need a result of Olariu on paw-free graphs.
Lemma 8 ([34])
Every connected paw-free graph is either triangle-free or complete multipartite.
We are ready to present our result for Contraction Blocker() restricted to -free graphs. This is the only result where we do not have a dichotomy due to one missing case.
Theorem 9.6
Let be a graph. If or , then Contraction Blocker) is polynomial-time solvable for -free graphs, otherwise it is NP-hard or co-NP-hard for -free graphs.
Proof
First assume that is connected. If is an induced subgraph of then we use Corollary 3. If is an induced subgraph of the paw, then we know from Lemma 8 that is either -free or complete multipartite. In the first case one must contract all the edges of an -free graph in order to decrease its clique number. Hence Contraction Blocker() is polynomial-time solvable for -free graphs. In the second case is -free, so we can use Corollary 3 again. If is neither an induced subgraph of nor of the paw, then we use Lemma 7.
Now assume that is not connected. If contains a connected component that is not an induced subgraph of or the paw then we use Lemma 7 again. Assume that each connected component of is an induced subgraph of or the paw. If or then we use Theorem 3.2 or Theorem 5.2, respectively. Hence, . In the first two cases and thus we can use Corollary 3, whereas we excluded the last case. ∎
9.3 When
Recall that Deletion Blocker() and Contraction Blocker() are called Critical Vertex and Contraction-Critical Edge, respectively, if . We need the following result announced in [36]; see [35] for its proof.
Theorem 9.7 ([36])
If a graph or of , then Critical Vertex and Contraction-Critical Edge restricted to -free graphs are polynomial-time solvable, otherwise they are NP-hard or co-NP-hard.
We also need the following result of Král’, Kratochvíl, Tuza, and Woeginger.
Theorem 9.8 ([28])
Let be a graph. If or of , then Coloring is polynomial-time solvable for -free graphs, otherwise it is NP-complete for -free graphs.
We also need the following lemma.
Lemma 9
Deletion Blocker()* is polynomial-time solvable for -free graphs.*
Proof
Let be a -free graph with and let be an integer. Consider an instance of Deletion Blocker(). We proceed as follows. Consider an optimal colouring of . Since is -free, the size of each colour class is at most . Moreover, the number of colour classes of size 1 is the same for every optimal colouring of . Let be this number. Hence, there are colour classes of size 2 and .
Now is a yes-instance if and only if we can obtain a graph from by deleting at most vertices such that . Since is also -free, the colour classes in any optimal colouring of have size at most 2 and thus, contains at most vertices. In other words, we need to delete at least vertices from in order to get such a graph . As such, is a no-instance if .
Next we will show that if , then is a yes-instance and this will complete the proof. If , we delete vertices representing colour classes of size 1. If , we delete the vertices representing the colour classes of size 1 and vertices of colour classes of size 2. In this way we obtain a graph whose chromatic number is exactly .
Due to the above, all we need to do is check if . This can be done in polynomial time, since we can compute in polynomial time due to Theorem 9.8.∎
Two disjoint subsets of vertices in a graph are complete if there is an edge between every vertex of and every vertex of . Lemma 8 implies the following lemma, which we use together with Corollary 3 and Lemma 9 to prove Lemma 11.
Lemma 10
The vertex set of every -free graph can be decomposed into two disjoint sets and such that is -free, is -free and and are complete to each other.
Proof
Let be a -free graph. Then is -free. By Lemma 8 every connected component of is triangle-free or complete multipartite. Let be the union of the vertices of all triangle-free components. Then -free, so is -free. Let . As every component of is complete multipartite, is -free. As , this means that is -free. Moreover, and are complete to each other in .∎
Lemma 11
Deletion Blocker()* is polynomial-time solvable for -free graphs.*
Proof
Let be an instance of Vertex Deletion Blocker(), where is -free. By Lemma 10, the vertex set of can be decomposed into two disjoint sets and such that is -free, is -free and and are complete to each other. The latter implies that . Moreover, this property is maintained when deleting vertices from . For each pair with we check by how much we can decrease using at most vertex deletions and by how much we can decrease using at most vertex deletions. We can do this in polynomial time by Corollary 3 and Lemma 9, respectively. We keep track of the maximum sum of these values. In the end, we are left to check if the value found is at least or not. Since the number of pairs is at most , the total running time is polynomial.∎
We can now state and prove the following two dichotomies.
Theorem 9.9
Let be a graph. Then the following holds:
- •
If or , then Deletion Blocker for -free graphs is polynomial-time solvable, and it is NP-hard or co-NP-hard otherwise.
**
- •
If , then Contraction Blocker for -free graphs is polynomial-time solvable for -free graphs, and it is NP-hard or co-NP-hard otherwise.
Proof
Let be a graph. If is neither an induced subgraph of nor of , then for both problems we can apply Theorem 9.7. If , then for both problems we apply Corollary 3. In the remaining case or . Then applying Lemma 11 gives us the desired dichotomy for Deletion Blocker(). And applying Theorem 3.2 gives us the desired dichotomy for Contraction Blocker() after recalling that cobipartite graphs are -free.∎
After proving Theorem 9.9 we have shown all six cases of Theorem 1.2. Note that, unlike the case (see Theorem 9.7), the complexity dichotomies of the problems Contraction Blocker() and Deletion Blocker() restricted to -free graphs are different when is disconnected.
10 Future Work
We aim to solve the blank entries in Table 1. In particular, we pose the following open problems:
- Q1.
Determine the complexity of Contraction Blocker() for interval graphs.
- Q2.
Determine the complexity of Deletion Blocker() for interval graphs.
We observe that the complexity of the two problems in Q1 and Q2 is unknown for interval graphs even if is fixed. We also aim to research the complexity of -Contraction Blocker() for bipartite graphs and chordal graphs, and the complexity of -Deletion Blocker() for perfect graphs and chordal graphs.
In addition to the above it would be interesting to generalize our results for the blocker problems restricted to -free graphs in Section 9 to families of more than one forbidden induced subgraph . However, we still need to complete one stubborn remaining case for one problem:
- Q3.
Determine the complexity of Contraction Blocker() for -free graphs.
We observe that it is not difficult to construct graph classes for which a blocker problem is tractable, but the original problem is NP-complete. However, we do not know of such examples of hereditary graph classes. Hence it would be interesting to solve the following question.
- Q4.
For , are Contraction Blocker() and Deletion Blocker() computationally hard on every hereditary graph class , for which Independent Set, Clique or Coloring, respectively, is NP-complete?
Several computationally hard cases of our dichotomies for -free graphs in Theorem 1.2 hold in fact even when or . In particular, from Theorems 9.7 and 9.9 we immediately deduce that if or , then 1-Deletion Blocker for -free graphs is polynomial-time solvable, and NP-hard or co-NP-hard otherwise. However, for the other five variants we still have a number of missing cases to solve.
Finally, we aim to determine a dichotomy with respect to -free graphs for the variant (), where consists of other graph operations, for instance when consists of an edge deletion. This variant has been less studied than the vertex deletion and edge contraction variant. The reason for this is that no class of -free graphs is closed under edge deletion, whereas such a class is closed under vertex deletion, and in the case when is a linear forest, under edge contraction as well. For we are close to a dichotomy. Recall that an edge of a graph is critical or contraction-critical if its deletion or contraction, respectively, reduces the chromatic number of by 1. It is known that an edge is contraction-critical if and only if it is critical [36]. Hence by Theorem 9.7 we only need to consider the cases where or . Bazgan et al. [2] showed that the edge deletion variant for chromatic number is polynomial-time solvable for threshold graphs, that is, for -free graphs, and NP-hard for cobipartite graphs, and thus for -free graphs. This means that the only two open cases for chromatic number are when or .
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1[1] L. Addario-Berry, W.S. Kennedy, A. D. King, Z. Li and B. Reed, Finding a maximum-weight induced k 𝑘 k -partite subgraph of an i 𝑖 i -triangulated graph. Discrete Applied Mathematics 158(7) (2010), 765–770.
- 2[2] C. Bazgan, C. Bentz, C. Picouleau and B. Ries, Blockers for the stability number and the chromatic number, Graphs and Combinatorics 31 (2015) 73–90.
- 3[3] C. Bazgan, S. Toubaline and Z. Tuza, The most vital nodes with respect to independent set and vertex cover, Discrete Applied Mathematics 159 (2011) 1933–1946.
- 4[4] C. Bentz, M.-C. Costa, D. de Werra, C. Picouleau and B. Ries, Weighted transversals and blockers for some optimization problems in graphs, Progress in Combinatorial Optimization, Wiley-ISTE, 2012.
- 5[5] R. Belmonte, P.A. Golovach, P. van ’t Hof and D. Paulusma, Parameterized complexity of two edge contraction problems with degree constraints, Proc. IPEC 2013, LNCS 8246 (2013) 16–27.
- 6[6] H.L. Bodlaender, R.H. Möhring, The pathwidth and treewidth of cographs, SIAM Journal on Discrete Mathematics 6 (1993) 181–188.
- 7[7] E. Boros, M.C. Golumbic and V.E. Levit, On the number of vertices belonging to all maximum stable sets of a graph, Discrete Applied Mathematics 124 (2002) 17-25.
- 8[8] A. Brandstädt, V.B. Le and J. Spinrad, Graph Classes: A Survey, SIAM Monographs on Discrete Mathematics and Applications, 1999.
