Abstract 1 Introduction 2 Main Result 3 Interval Graphs 4 Summary References Appendix A Clique Node Deletion

A Note on the Complexity of Defensive Domination

Steven Chaplick ORCID Maastricht University, The Netherlands Grzegorz Gutowski ORCID Institute of Theoretical Computer Science, Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland Tomasz Krawczyk ORCID Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland
Abstract

In a graph G, a k-attack A is any set of at most k vertices and -defense D is a set of at most  vertices. We say that defense D counters attack A if each aA can be matched to a distinct defender dD with a equal to d or a adjacent to d in G. In the defensive domination problem, we are interested in deciding, for a graph G and positive integers k and  given on input, if there exists an -defense that counters every possible k-attack on G. Defensive domination is a natural resource allocation problem and can be used to model network robustness and security, disaster response strategies, and redundancy designs.

The defensive domination problem is naturally in the complexity class Σ2𝖯. The problem was known to be 𝖭𝖯-hard in general, and polynomial-time algorithms were found for some restricted graph classes. In this note, we prove that the defensive domination problem is Σ2𝖯-complete.

We also introduce a natural variant of the defensive domination problem in which the defense is allowed to be a multiset of vertices. This variant is also Σ2𝖯-complete, but we show that it admits a polynomial-time algorithm in the class of interval graphs. A similar result was known for the original setting in the class of proper interval graphs.

Keywords and phrases:
graph domination, computational complexity
Funding:
Grzegorz Gutowski: Partially supported by grant no. 2023/49/B/ST6/01738 from National Science Centre, Poland.
Tomasz Krawczyk: Partially supported by grant no. 2024/53/B/ST6/02558 from National Science Centre, Poland.
Copyright and License:
[Uncaptioned image] © Steven Chaplick, Grzegorz Gutowski, and Tomasz Krawczyk; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Theory of computation
Editors:
Paweł Gawrychowski, Filip Mazowiecki, and Michał Skrzypczak

1 Introduction

All graphs discussed in this paper are finite and simple. The vertex set and edge set of a graph G are denoted by V(G) and E(G). For a subset UV(G), G[U] denotes the subgraph of G induced by U, and GU denotes the subgraph G[V(G)U], which is shortened to Gv when U={v}. The neighborhood of a vertex v, denoted by NG(v), comprises of vertices adjacent to v and the closed neighborhood of v is NG[v]=NG(v){v}. The closed neighborhood and the neighborhood of a set UV(G) of vertices are defined as NG[U]=vUNG[v] and NG(U)=NG[U]U, respectively. The subscript G can be dropped if the graph is clear from the context. A graph on t vertices with all (t2) edges is a clique Kt. When G[U] is isomorphic to Kt for some set U of vertices, we say that G contains Kt as a subgraph. The clique problem asks for the maximum t such that Kt is contained as a subgraph in a given graph. A set DV(G) is a dominating set when NG[D]=V(G). The graph domination problem asks for the minimum size of a dominating set in a given graph and is a classical problem in graph theory and combinatorial optimization. We use the following specification of the problem.

Problem 1.

DominatingSet

Input:

A graph G and a positive integer

Output:

𝖸𝖾𝗌 if and only if G admits a dominating set of size at most

This problem has broad practical applications in resource allocation, network design, analysis and security. It is also of theoretical interest, as it is one of the first problems known to be 𝖭𝖯-complete, see [7], and is used as a base for countless reductions. Consult [8, 9, 10] for various versions and applications of the domination problem.

Graph domination can be understood through the analogy in which the vertices of a graph are under threat of some attack and defenders need to be placed in the vertices so that each vertex either has a defender stationed directly in it or in an adjacent vertex. This concept is useful in network security, facility location problems (positioning service centers), and disaster response strategies (deploying rescue teams). Presented in this way, the DominatingSet problem looks for a minimum number of defenders that can counter any attack on a single vertex.

Farley and Proskurowski [6] proposed the following extension of the problem, called defensive domination, where we prepare for a simultaneous attack on at most k vertices. We say that any set A of at most k distinct vertices in G is a k-attack. An -defense D is a set of at most  distinct vertices of G and corresponds to placing defenders, one in each vertex of D. We say that defense D counters attack A if there is a matching between A and D such that each aA is matched to a distinct defender dD with a equal to d or a adjacent to d in G. A defense D that counters every possible k-attack in G is called a k-defensive dominating set. This extension is natural and meets the redundancy requirements usual for all applications of the domination problem. We use the following formal specification of the problem.

Problem 2.

DefensiveDominatingSet

Input:

A graph G and positive integers k and

Output:

𝖸𝖾𝗌 if and only if G admits an -defense that counters every k-attack in G

The parametrized version of the problem, where the size of the attack is an external parameter and not a part of the input is also of interest.

Problem 3.

k-DefensiveDominatingSet

Input:

A graph G and a positive integer

Output:

𝖸𝖾𝗌 if and only if G admits an -defense that counters every k-attack in G

Observe that k-DefensiveDominatingSet for k=1 is exactly DominatingSet. Dereniowski, Gavenčiak, and Kratochvíl [3] proposed a further extension of the problem, which seems a bit technical, but was successfully applied to study a variant of the cops and robbers game. The proposed extension of defensive domination allows the placement of multiple defenders in a single vertex of the graph and limits the possible attacks to the ones that are explicitly specified on the input. A multiset -defense D places defenders in total at the vertices of G, each vertex getting as many defenders as its multiplicity in D. Multiset defense D counters attack A if each aA can be matched to a distinct defender stationed in a, or any vertex adjacent to a. The formal specification of the problem proposed by Dereniowski, Gavenčiak, and Kratochvíl [3] follows.

Problem 4.

𝒜-DefensiveDominatingMultiset

Input:

A graph G, a set of attacks 𝒜2V(G), multisets D1 and D2 of vertices of G, and a positive integer

Output:

𝖸𝖾𝗌 if and only if G admits a multiset -defense D with D1DD2 that counters every attack A𝒜

Observe, that we do not allow for multiset attacks, as it would lead to a different problem.

We believe that allowing for multiset defenses is an interesting extension and allows for various applications. We propose the following natural extension of the defensive domination problem, that allows for multiset defenses, but drops other technical conditions introdued by Dereniowski, Gavenčiak, and Kratochvíl.

Problem 5.

DefensiveDominatingMultiset

Input:

A graph G and positive integers k and

Output:

𝖸𝖾𝗌 if and only if G admits a multiset -defense that counters every k-attack in G

To illustrate the strength of this extension, consider the following example. Graph K1,t is a complete bipartite graph with bipartition classes of sizes 1 and t. Note that any 2-defensive dominating set on K1,t has at least t defenders, but it is enough to use 2 defenders in the multiset setting. In the proof of the main result of this paper, we focus on DefensiveDominatingSet, but the hardness also applies to DefensiveDominatingMultiset, which we believe should attract more attention.

As DominatingSet is 𝖭𝖯-complete, it is straightforward that all mentioned domination problems are 𝖭𝖯-hard. Both of the problems k-DefensiveDominatingSet and 𝒜-DefensiveDominatingMultiset are in fact 𝖭𝖯-complete, since one can guess a defense and check if it counters all possible attacks in polynomial time (for k-DefensiveDominatingSet there are O(nk) possible attacks to check which is polynomial in n when k is fixed). On the other hand, as DefensiveDominatingSet is naturally expressed as:

DV(G),|D|:AV(G),|A|k:D counters A in G,

we easily get that DefensiveDominatingSet is in the second level of polynomial hierarchy class Σ2𝖯. Consult the textbook by Arora and Barak [1, Chapter 5] for an introduction of the polynomial hierarchy. Schaefer and Umans [13, 14, 15] give an extensive list of complete problems for different classes in the polynomial hierarchy. For a very brief introduction, Σ2𝖯 is defined as 𝖭𝖯𝖭𝖯 – a class of languages decidable in polynomial time by nondeterministic Turing machines with access to 𝖭𝖯-oracle, where 𝖭𝖯-oracle allows to test any language in 𝖭𝖯 in a single step of execution. The canonical complete problem for Σ2𝖯 is the following.

Problem 6.

Existential-2-Level-SAT

Input:

Formula φ(x1,,xa,y1,,yb) with variables in two disjoint sets {x1,,xa} and {y1,,yb}

Output:

𝖸𝖾𝗌 if and only if the following Boolean formula is true.

x1,x2,,xa:y1,y2,,yb:φ(x1,,xa,y1,,yb)

It was indpendently proved by Stockmeyer [16] and Wrathall [17] that the class Σ2𝖯 is exactly the class of languages reducible to Existential-2-Level-SAT via polynomial-time many-one reductions. Clearly, the following nondeterministic algorithm using 𝖭𝖯-oracle solves DefensiveDominatingSet: algorithm first guesses a set of defenders D and then uses 𝖭𝖯-oracle to test whether there exists an attack of size at most k not defended by D. This fact makes Σ2𝖯 a natural complexity class for DefensiveDominatingSet problem.

Ekim, Farley, and Proskurowski [4] showed that DefensiveDominatingSet is unlikely to be in 𝖭𝖯. The reason is that the following problem that corresponds to checking if a given defense D counters every k-attack on G is already 𝖼𝗈-𝖭𝖯-complete.

Problem 7.

GoodDefense

Input:

A graph G, a subset D of vertices, and a positive integer k

Output:

𝖸𝖾𝗌 if and only if defense D counters every k-attack in G

For any defense set, or multiset, D, and any set of vertices XV(G), let countD(X) denote the number of elements (counting multiplicities for multisets) in DX. The following connection between defensive domination and Hall’s condition was already observed in [4].

Observation 1 (Ekim, Farley, Proskurowski [4]).

The following conditions are equivalent:

  • Defense D counters every k-attack in G.

  • For every k-attack A we have |A|countD(N[A]).

This draws our attention to the complementary problem of GoodDefense and a very similar problem that is known to be 𝖭𝖯-complete and 𝖶[𝟣]-hard.

Problem 8.

BadDefense

Input:

A graph G, a subset D of vertices, and a positive integer k

Output:

𝖸𝖾𝗌 if there exists k-attack A with |A|>countD(N[A])

Problem 9.

HallSet

Input:

A bipartite graph G with bipartition classes U and W and a positive integer k

Output:

𝖸𝖾𝗌 if and only if there exists XU with |NG(X)|<|X|k

In [2, Exercise 13.28], a parametrized reduction is given from Clique to HallSet. HallSet has an easy parametrized reduction to BadDefense. All of these observations allow for the following conclusion.

Lemma 2 (Theorem 2.3 in Ekim, Farley, Proskurowski [4]).

GoodDefense is 𝖼𝗈-𝖭𝖯-complete. BadDefense is 𝖭𝖯-complete and 𝖶[𝟣]-hard when parametrized by k.

When k is an external parameter of the problem, k-DefensiveDominatingSet is in 𝖭𝖯, and it remains 𝖭𝖯-complete even when the input graph is restricted to split graphs [4], or bipartite graphs [11]. On the positive side, DefensiveDominatingSet admits polynomial-time algorithms when the input graph is restricted to cliques, cycles, trees [6], co-chain graphs, threshold graphs [4], or proper interval graphs [5].

The main result of this paper is the following.

Theorem 3.

DefensiveDominatingSet and DefensiveDominatingMultiset are Σ2𝖯-complete.

The introduced multiset setting not only may better fit some applications, but might also be more approachable algorithmically. For example, in Section 3 we investigate the multiset defensive domination problem on the class of interval graphs. A graph G is an interval graph when each vertex vV(G) corresponds to a closed interval Iv, and {u,v}E(G) if and only if IuIv. We prove the following.

Theorem 4.

DefensiveDominatingMultiset is in 𝖯 when the input graphs are restricted to the class of interval graphs.

A similar result for DefensiveDominatingSet was shown for proper interval graphs by Ekim, Farley, Proskurowski, and Shalom [5]. The complexity of DefensiveDominatingSet for interval graphs remains unknown.

The proof of the main theorem is based on a reduction of the following problem, which was shown to be Σ2𝖯-complete by Rutenburg [12].

Problem 10.

CliqueNodeDeletion

Input:

A graph G and positive integers s and t

Output:

𝖸𝖾𝗌 if and only if G admits a set X of at most s vertices such that GX does not contain Kt as a subgraph

Theorem 5 (Theorem 6 in Rutenburg [12]).

CliqueNodeDeletion is Σ2𝖯-complete.

As the original paper includes only an idea of the proof that requires some minor alterations, we have decided to present a streamlined proof of Theorem 5 in Appendix A.

2 Main Result

We are ready for the proof of the main result. See 3

Proof.

We present a reduction from CliqueNodeDeletion. Assume that we are given an instance G,s,t, where s is the number of vertices to remove from G and t is the size of clique to avoid as a subgraph. For technical reasons, we assume that t4. Let n=|V(G)| denote the number of vertices in G.

We construct an equivalent instance G,k, of DefensiveDominatingSet. We set the maximum size of an attack k=n+s, the maximum size of a defense =4(n+s)+nt(t1), and construct the graph G as depicted in Figure 1:

  • For each vertex vV(G), we introduce two vertices v and v′′ representing v in G. Set W denotes vertices v, v′′ introduced for all vV(G).

  • For each edge e=(u,v) in E(G), we introduce the vertex e and add the edges joining e with four vertices u, u′′, v, v′′ in W. Set F denotes vertices e introduced for all eE(G).

  • We introduce four independent sets: I1 of size n+s; I2 of size n+s(t2); I3 of size n+s; and I4 of size n+s+.

  • We introduce three cliques: Q1 of size n+s; Q2 of size n+s(t+1); and Q3 of size n+s.

  • For each vertex vV(G), we introduce a complete bipartite graph with one bipartition class Iv of size (t2), and the other class Iv of size t. Set IV denotes the union of sets Iv for all vV(G) and IV denotes the union of sets Iv for all vV(G).

  • We add edges of complete bipartite graphs given by the biparition classes: (Q1,I1); (Q2,Q1I2FIV); (Q3,I3); (W,Q3I4); and ({v,v′′},Iv) for each vV(G).

Figure 1: Reduction: The edge e=(u,v) of G is represented by the edges between e and v,v′′,u,u′′ in G. The defenders are represented by crossed circles.

We claim that s vertices can be removed from G so that the resulting graph does not include Kt as a subgraph if and only if there is an -defense that counters every k-attack in G. Before presenting the proof, let us give some ideas on the role of different “gadgets” used in the construction of G. The sets I force any successful defense to position many defenders in their neighborhood. This allows for good control over the degrees of freedom in successful -defenses. The set W contains two copies of each vertex of G and the defense is forced to position at least one defender on them. We use the possibility of choosing some of the second copies in each pair to express the selection of a clique hitting set. We are able to limit the “dangerous” choices of k-attacks so that they express the choice of edges of a clique Kt in the set F. We independently prove the implications in both directions.

() Let X be a solution to the instance G,s,t. We have |X|=s and GX does not include Kt as a subgraph. We construct the following defense D:

D=Q1Q2Q3IV{v:vV(G)}{v′′:vX}.

Note that D positions exactly n+s defenders on W. Additionally, D has nt defenders on IV and 3(n+s)(t+1) defenders on Q1Q2Q3, which gives defenders in total. We show that D counters every attack of size at most n+s. Suppose for a contradiction that A is an inclusion minimal attack with countD(N[A])<|A|n+s. First, we observe that every vertex in the sets W, I1, I3, I4, Q1, Q2, and Q3 is adjacent to at least n+s defenders (either from the set Q1, Q2, Q3 or W). If any of these vertices is included in A, then countD(N[A])n+s|A|. We conclude that

AI2FIVIV.

As every vertex in IV is in D, we do not have AIV and hence A(I2FIV). In particular, Q2N[A], and hence countD(N[A])|Q2|=n+s(t+1). Since every vertex in IV is adjacent to additional t+1 defenders in WIV, we have AIV=. Now, N[IV]A=IVA, and IVD, so by the minimality of A we get AIV=. We conclude that

AI2F.

As t+1<(t2) for t4 and |A|>countD(N[A])n+s(t+1)>n+s(t2)=|I2|, we also have AF. As A includes at least one vertex in F, N[A] includes at least two vertices in WD and |A|>n+s(t+1)+2. We conclude that

|AF||A||I2|>n+s(t+1)+2(n+s(t2))=(t2)(t1)=(t12).

Now, as A includes more than (t12) vertices in F, N[A] includes at least t vertices in WD, and |A|>n+s(t+1)+t. Hence, |A|=n+s and I2A, as otherwise we would have countD(N[A])>n+s. We call an attack A to be serious if

I2AI2F,|AF|=(t2).

We have shown that attack A is serious. Any (t2) edges in G span at least t+1 vertices or span t vertices that form a clique Kt in G. As we know that GX does not contain Kt as a subgraph, we get that if the edges span only t vertices then at least one of them is in X. In either case, we get countD(N[A])n+s(t+1)+(t+1)=n+s and D counters A.

() Let D be an -defense that counters every k-attack in G. We make the following observations.

  1. 1.

    As D counters attack I1, there are at least n+s defenders in I1Q1.

  2. 2.

    As D counters attack I3, there are at least n+s defenders in I3Q3.

  3. 3.

    As D counters every possible (n+s)-attack in I4D, there are at least n+s defenders in W.

  4. 4.

    For every vV(G), as D counters attack Iv, there are at least t defenders in IvIv. Thus, in total, there are at least nt defenders in IVIV.

  5. 5.

    By calculation, there are at most n+s(t+1) defenders in I2Q2F.

  6. 6.

    For every vV(G), as D counters attack I2Iv and there are at most n+s(t+1) defenders in I2Q2, there are at least t+1 defenders in IvIv{v,v′′}. As |Iv|=t, at least one of the defenders is in Iv{v,v′′}.

We now construct a modified defense D and claim that D counters every serious attack on G. As we focus on serious attacks, and the defenders in IV are not used to counter any serious attack, we move some of them. For each vV(G) if vD and v′′D, we move one defender from Iv to v (guaranteed to be there by point 6). Observe that even after this move there are at least t defenders in IvIv. As there are still at least nt+(n+s)+(n+s) defenders in IVIVI1Q1I3Q3, there are at most (n+s)+(n+s(t+1) in I2Q2FW. Second, since serious attacks include only vertices in I2F, and vertices in Q2 dominate I2F, and there are at most n+s(t+1) defenders in I2Q2F, we move all defenders from I2 and F to Q2. Third, while |DW|>n+s, we select any vV(G) with defenders both in v and v′′ and move the defender from v′′ to Q2. The resulting defense D has the property that it also counters every serious attack, as D did.

The resulting defense D has exactly n+s defenders in W, at most n+s(t+1) defenders in Q2, at least one defender in each {v,v′′} for every vV(G), and counters every serious attack. Let X be a set of vertices v in V(G) for which both v and v′′ are in D. There are exactly s such vertices. We claim that GX does not contain Kt as a subgraph. Indeed, given a t-element set Q of vertices of G with QX= and G[Q] isomorphic to Kt, we can create a k-attack composed of I2 and the set of (t2) vertices e representing edges of G[Q]. This attack has size n+s and has at most n+s(t+1) neighboring defenders in Q2 and exactly t neighboring defenders in W. Thus, this serious attack is not countered by D, which contradicts the construction of D.

We leave it to the reader to verify that exactly the same reduction also shows that DefensiveDominatingMultiset is also a Σ2𝖯-complete problem.

3 Interval Graphs

Building on the work of Ekim, Farley, Proskurowski, and Shalom [5], who presented a greedy algorithm for DefensiveDominatingSet on proper interval graphs, we develop a similar greedy strategy for DefensiveDominatingMultiset on general interval graphs.

For the remainder of this section, let G be an interval graph given by its interval representation: each vertex vV(G) corresponds to a closed interval Iv, and {u,v}E(G) if and only if IuIv. We assume that this representation ensures that no two distinct intervals share an endpoint. For any bounded closed set S, let left(S) and right(S) denote the minimum and the maximum element in S, respectively. For any set (or multiset) of intervals X, let sum(X)=IXI denote their union, and let span(X) be the minimum closed interval containing every interval in X, that is, the interval [left(X),right(X)]. For a set YV(G), we define sum(Y)=sum({Iv:vY}) and span(Y)=span({Iv:vY}). A set (or multiset) of intervals X is proper if no interval in X is a proper subset of another interval in X. We say that a set YV(G) is proper, when {Iv:vY} is proper. The algorithm presented by Ekim, Farley, Proskurowski, and Shalom [5] for DefensiveDominatingSet worked under the condition that V(G) is proper.

Our first observation is that in the multiset setting we can focus on constructing proper defenses.

Observation 6.

For any multiset defense D, there exists a proper multiset defense D such that |D|=|D| and D counters any attack that D counters.

Proof.

Consider a multiset defense D. If it is not proper, then there exist two vertices u,vD such that IuIv. Let D be the multiset defense obtained from D by replacing every copy of u with an additional copy of v. Clearly, |D|=|D|. Since IuIv, we have that N[u]N[v] and that any attack A that is countered by D using defenders in u is countered by D using added defenders in v. Therefore, D counters every attack countered by D. Observe that this replacement increases the total length of the intervals that represent vertices in the defense. Thus, repeating this replacement procedure eventually stops and yields a proper defense that counters any attack that the original defense counters.

Note that Observation 6 holds specifically for the multiset setting and does not have a direct analogue for DefensiveDominatingSet. The next observation is that sets with smaller union are more dangerous for any defense than those with larger union.

Observation 7.

For any defense D and two sets A1, A2 with countD(N[A1])<|A1|, |A1||A2|, and sum(A2)sum(A1) we have countD(N[A2])<|A2|.

Proof.

We have that countD(N[A2]) is the number of intervals in D that have a nonempty intersection with sum(A2) which is a subset of sum(A1). Thus,

countD(N[A2])countD(N[A1])<|A1||A2|.

Let x=right(Iv) for some vertex vV(G). Let Vx={vV(G):right(Iv)x} denote the nonempty set of vertices that lie completely to the left of x in the representation. Let c=|Vx| and for every integer 1ic we define the i-th block at x, denoted Bx,i, in the following way. Let v1,v2,,vc be Vx arranged in a sequence sorted descending by the left endpoint of the representing interval, that is, left(Iu)>left(Iw) if and only if u appears earlier than w in the sequence. We select Bx,i={v1,v2,,vi} to be the first i elements in the sequence. Note that if defined, Bx,i contains exactly i vertices, Bx,i+1 is a superset of Bx,i, and Bx,i maximizes left(span(X)) among all subsets XVx with |X|=i.

We say that D is a k-block defense if it counters every attack Bx,i for every possible right endpoint x and every ik.

Lemma 8.

A proper k-block defense D counters every k-attack in G.

Proof.

Assume to the contrary that a proper k-block defense D does not counter some k-attack. By Observation 1 we have a set A with countD(N[A])<|A|=mk. Among such sets, we select A with the minimum size m.

Let wA be the vertex that maximizes right(Iw) among the vertices in A. Let x=right(Iw), c=|Vx|, and v1,v2,,vc be Vx arranged in a sequence sorted descending by the left endpoint of the representing interval. All elements of A are represented to the left of x, so we have mc. Recall that Bx,i={v1,v2,,vi} for every 1ic. By the assumption of the lemma, we know that ABx,m. Since D counters Bx,m, we have countD(N[A])<countD(N[Bx,m]) and there is dD with dN[Bx,m] and dN[A]. Every interval representing a vertex in A is either completely to the left or completely to the right of Id. Let

A1={vA:right(Iv)<left(Id)}andA2={vA:right(Id)<left(Iv)}.

Since xIw and xId, we have wA2. Since left(span(A))<left(span(Bx,m)), we have A1. We have partitioned A into two nonempty subsets A1 and A2.

By the minimality of A we have countD(N[A1])|A1| and countD(N[A2])|A2|. We get that there is at least one dDN[A1]N[A2], as otherwise we would have countD(N[A])=countD(N[A1])+countD(N[A2])|A1|+|A2|=|A|. Interval Id intersects both span(A1) and span(A2). This allows us to write the following inequalities:

left(Id)<right(span(A1))<left(Id)<right(Id)<left(span(A2))<right(Id),

and conclude that Id is a proper subset of Id contradicting with D being a proper defense.

We are ready for the proof of the main result of this section. See 4

Proof.

The proposed algorithm, see Algorithm 1 for the pseudocode, works as follows. It assumes that G has n vertices and is given in the interval representation by the intervals I1,I2,,In. It sorts the intervals ascending by their right endpoints. Then, for every 1in, let x be the right endpoint of the i-th interval in the list. The algorithm calculates the number count of defenders missing to counter every m-block attack Bx,m for mk. Then, it selects the interval d that maximizes the right endpoint among all intervals that include x. The algorithm adds count copies of the interval d to D.

Algorithm 1 Greedy Multiset Defensive Domination in Interval Graphs.

Clearly, Algorithm 1 runs in polynomial time. The algorithm only adds inclusion maximal intervals to D, so the constructed multiset defense D is proper. The algorithm explicitly adds the required number of defenders to counter every possible attack Bx,m, so D is also k-block. Thus, by Lemma 8 we get that D counters every k-attack in G.

For the proof that the defense D is of minimum size, let =|D|, J1,J2,,J be the multiset of intervals in D sorted ascending by their left endpoints. Assuming to the contrary, let D be another proper defense that counters every k-attack with =|D|<|D| and K1,K2,,K be the multiset of intervals in D sorted ascending by their left endpoints. Among such defenses, select one that maximizes the number p such that Ji=Ki for all 1ip. We have p<<. Let d=Jp+1 and d=Kp+1. We do not have dd, as otherwise we could exchange d for d in D and get another defense that counters every k-attack of the same size, but with larger p. We also do not have dd, as the algorithm only adds inclusion maximal intervals to D. Let x,m be such that the algorithm added d to D when considering attack Bx,m.

If left(d)>left(d) then right(d)>right(d) and as the algorithm did not add d to D we have left(d)>x. This means that D has exactly p intervals K1,,Kp=J1,,Jp with the left endpoint less than or equal to x. The algorithm calculated that these intervals do not counter Bx,m and no other interval in D intersects span(Bx,m). We conclude that D does not counter Bx,m, a contradiction.

If left(d)<left(d) then right(d)<right(d) and we can exchange d for d in D and get another defense D′′ that counters every k-attack with |D′′|=|D| but with bigger p. Indeed, we can show that the resulting defense D′′ satisfies countD′′(N[By,q])q for every possible right endpoint y and every qk. For y<x, this follows from the fact that D′′ agrees with D on the first p intervals that are enough to counter these attacks. For yx, we have dN[By,q]dN[By,q] and countD′′(N[By,q])countD(N[By,q])q. We get a contradiction with the choice of D.

We claim that Algorithm 1 can be implemented to run in O(nk) time using standard techniques, and we omit the details of this implementation.

4 Summary

In this work, we have shown that both DefensiveDominatingSet and DefensiveDominatingMultiset are Σ2𝖯-complete. For the multiset variant of the problem, in the class of interval graphs, we have indicated a polynomial-time algorithm. This algorithm does not work in the original setting where at most one defender can be located at a single vertex. Furthermore, we do not even know if GoodDefense admits a polynomial-time algorithm in the class of interval graphs. We would like to see the complexity status of DefensiveDominatingSet resolved in the class of interval graphs.

We also believe that potential applications in the facility location problem should justify the investigation of defensive domination problems in the class of planar graphs. Note that the reductions presented for Σ2𝖯-completeness of DefensiveDominatingSet and DefensiveDominatingMultiset or the 𝖶[𝟣]-hardness of BadDefense construct graphs with large cliques and cannot be applied to show the hardness in the class of planar graphs. There is a natural dynamic programming 𝖥𝖯𝖳-algorithm that checks BadDefense when parametrized by the tree-width of the input graph. When looking for dangerous k-attacks against a fixed defense, it is enough to consider attacks A such that N[A] is connected. This means that in a planar graph, we can fix some outerplanar decomposition of the graph and only consider k-attacks that span at most 2k1 adjacent layers of the outerplanar decomposition. As the tree-width of such subgraphs is bounded, we obtain a simple 𝖥𝖯𝖳-algorithm that checks BadDefense when parametrized by k in the planar graphs. This shows that in the parametrized sense, defensive domination problems might be easier in planar graphs than they are in general graphs. This motivates the following questions. Does GoodDefense admit a polynomial-time algorithm in the class of planar graphs? What is the complexity of DefensiveDominatingSet and DefensiveDominatingMultiset in the class of planar graphs? Both problems are 𝖭𝖯-hard, but we do not know if they are Σ2𝖯-complete.

References

  • [1] Sanjeev Arora and Boaz Barak. Computational Complexity: A Modern Approach. Cambridge University Press, 2009.
  • [2] Marek Cygan, Fedor V. Fomin, Łukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michał Pilipczuk, and Saket Saurabh. Parameterized Algorithms. Springer, 2015. doi:10.1007/978-3-319-21275-3.
  • [3] Dariusz Dereniowski, Tomáš Gavenčiak, and Jan Kratochvíl. Cops, a fast robber and defensive domination on interval graphs. Theoretical Computer Science, 794:47–58, 2019. Special Issue on Theory and Applications of Graph Searching. doi:10.1016/j.tcs.2018.09.031.
  • [4] Tınaz Ekim, Arthur M. Farley, and Andrzej Proskurowski. The complexity of the defensive domination problem in special graph classes. Discrete Mathematics, 343(2):111665, 2020. doi:10.1016/j.disc.2019.111665.
  • [5] Tınaz Ekim, Arthur M. Farley, Andrzej Proskurowski, and Mordechai Shalom. Defensive domination in proper interval graphs. Discrete Applied Mathematics, 331:59–69, 2023. doi:10.1016/j.dam.2023.01.016.
  • [6] Arthur M. Farley and Andrzej Proskurowski. Defensive domination. In SEICCGTC 2004: 35th Southeastern International Conference on Combinatorics, Graph Theory and Computing, volume 168 of Congressus Numerantium, pages 97–107, 2004.
  • [7] Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, 1979.
  • [8] Teresa W. Haynes, Stephen T. Hedetniemi, and Michael A. Henning. Topics in Domination in Graphs. Developments in Mathematics. Springer, 2020. doi:10.1007/978-3-030-51117-3.
  • [9] Teresa W. Haynes, Stephen T. Hedetniemi, and Michael A. Henning. Structures of Domination in Graphs. Developments in Mathematics. Springer, 2021. doi:10.1007/978-3-030-58892-2.
  • [10] Teresa W. Haynes, Stephen T. Hedetniemi, and Michael A. Henning. Domination in Graphs: Core Concepts. Springer Monographs in Mathematics. Springer, 2023. doi:10.1007/978-3-031-09496-5.
  • [11] Michael A. Henning, Arti Pandey, and Vikash Tripathi. More on the complexity of defensive domination in graphs. Discrete Applied Mathematics, 362:167–179, 2025. doi:10.1016/j.dam.2024.11.023.
  • [12] Vladislav Rutenburg. Complexity of generalized graph coloring. In MFCS 1986: 12th Symposium on Mathematical Foundations of Computer Science, volume 233 of LNCS, pages 573–581, 1986. doi:10.1007/BFb0016284.
  • [13] Marcus Schaefer and Christopher Umans. Completeness in the polynomial-time hierarchy: Part I: A compendium. SIGACT News, 33(3):32–49, 2002. doi:10.1145/582475.582484.
  • [14] Marcus Schaefer and Christopher Umans. Completeness in the polynomial-time hierarchy: Part II. SIGACT News, 33(4):22–36, 2002. doi:10.1145/601819.601829.
  • [15] Marcus Schaefer and Christopher Umans. Completeness in the polynomial-time hierarchy: A compendium, 2008. URL: https://ovid.cs.depaul.edu/documents/phcom.pdf.
  • [16] Larry J. Stockmeyer. The polynomial-time hierarchy. Theoretical Computer Science, 3(1):1–22, 1976. doi:10.1016/0304-3975(76)90061-X.
  • [17] Celia Wrathall. Complete sets and the polynomial-time hierarchy. Theoretical Computer Science, 3(1):23–33, 1976. doi:10.1016/0304-3975(76)90062-1.

Appendix A Clique Node Deletion

The main result of this paper is proved by a reduction from CliqueNodeDeletion to DefensiveDominatingSet. Clique Node Deletion Problem was first introduced by Rutenburg [12] in a more general setting called Generalized Node Deletion. Rutenburg gives an idea for a proof [12, Theorem 6] that CliqueNodeDeletion is Σ2𝖯-complete. As CliqueNodeDeletion is an important intermediate problem for our result, we present a streamlined proof based on Rutenburg’s idea.

The proof is based on a reduction from the following problem, which is a variation on the quantified boolean formula satisfaction problem. It is a natural Σ2𝖯-complete problem with an easy reduction from Existential-2-Level-SAT [16].

Problem 11.

Existential-2-Level-3-CNF

Input:

3-CNF formula φ(x1,,xa,y1,,yb) with variables in two disjoint sets {x1,,xa} and {y1,,yb}

Output:

𝖸𝖾𝗌 if and only if the following Boolean formula is true.

x1,x2,,xa:¬y1,y2,,yb:φ(x1,,xa,y1,,yb)
Figure 2: Rutenburg’s reduction. The part of G corresponding to the clauses C1(x1¬x2y1) and C2(¬x2¬x3¬y3). Ugly vertices are represented by crossed circles.

We are now ready to present the proof of the following theorem. See 5

Proof.

We reduce from Existential-2-Level-3-CNF to CliqueNodeDeletion. Assume that we are given two disjoint sets of variables X={x1,,xa}, Y={y1,,yb}, and a set of clauses 𝒞={C1,,Cc} with each clause having exactly three occurrences of three distinct variables in XY. For technical reasons, we assume that c>6, as otherwise there are at most 18 variables and we can simply check all the possibilities. We set s=ac+3c, t=b+c, and construct a graph G such that G admits a subset X of vertices with |X|s such that GX does not contain Kt as a subgraph if and only if the following formula

x1,x2,,xa:¬y1,y2,,yb:C1C2Cc

is true. The graph G is constructed in the following steps. Consult Figure 2 for an example.

  1. 1.

    For each variable xi (1ia) we introduce Gxi: a copy of Kc,c with one bipartition class called positive and the other class called negative. We number positive vertices from 1 to c and number negative vertices from 1 to c.

  2. 2.

    For each variable yj (1jb) we introduce Gyj: two independent vertices, one called positive and the other called negative.

  3. 3.

    For each clause Ck (1kc) we introduce GCk: a copy of K3,3 with one bipartition class called good and the other class called bad. We select one of the bad vertices to be ugly. In Ck there are 3 occurrences of variables. For each such occurrence, we assign a distinct good vertex in GCk. We call the good vertices assigned to variables in X (Y) to be X-good (Y-good).

  4. 4.

    For each edge (u,v) in every Gxi and every GCk we add a copy of Kt on u, v and additional t2 new vertices.

  5. 5.

    For each GCk with g X-good vertices we compose set Z of all X-good vertices in GCk and every positive (negative) vertex numbered k in Gxi with xi having a positive (negative) occurrence in Ck. Set Z has exactly 2g elements. We add a copy of Kt1+g on vertices in Z and additional t1g new vertices. We call this added clique QCk.

  6. 6.

    For each positive (negative) occurrence of yj in Ck, let u be the Y-good vertex in GCk assigned to this occurrence. We add an edge between u and the positive (negative) vertex in Gyj. We also add edges between u and both vertices in every other Gyj for jj.

  7. 7.

    For each clause Ck, let u be the ugly vertex in GCk. We add edges between u and both vertices in every Gyj.

  8. 8.

    For every 1j<jb, we add all four edges between any of the two vertices in Gyj and any of the two vertices in Gyj.

  9. 9.

    Finally, for every 1k<kc, every bad or Y-good vertex u in GCk, and every bad or Y-good vertex u in GCk we add an edge between u and u.

Observe that G contains many copies of Kt. We distinguish four types of such cliques:

  1. (A)

    A clique that contains a positive and a negative vertex in a single Gxi. The common neighborhood of such a pair of vertices is the set of additional vertices added in step 4 of the construction.

  2. (B)

    A clique that contains a good and a bad vertex in a single GCk. The common neighborhood of such a pair of vertices is the set of additional vertices added in step 4 of the construction.

  3. (C)

    A clique that contains an X-good vertex in some GCk and is not of type B. As this clique does not contain any bad vertex in GCk, it must be a subclique of QCk constructed in step 5.

  4. (D)

    Other. Observe that every other clique of size t can contain only Y-good or ugly vertices in GCj or positive or negative vertices in Gyi.

We independently prove the implications in both directions.

() Let ν1,ν2,,νa be a valuation of x1,x2,,xa such that formula

y1,y2,,yb:C1C2Cc

is false. We construct a set X of vertices in V(G) in the following way. From each Gxi we select all the c negative vertices if νi is true and all c positive vertices if νi is false. For each Ck if Ck is satisfied by any of the variables xi=νi we select all 3 good vertices. Otherwise, we select all 3 bad vertices. The constructed set X has ac+3c vertices. We claim that GX does not contain Kt as a subgraph. Each clique of type A or B is removed by the selection of vertices. Each clique QCk of type C corresponding to a clause Ck with g occurrences of variables in X is removed, as we either remove g vertices corresponding to the false occurrences of variables in variable gadgets, or all good vertices in clause gadgets GCk.

Now, assuming to the contrary, let Q be some clique of type D that remains in GX. It is clear that Q can have at most one vertex in each Gyj and at most one vertex in each GCk. As t=b+c, Q has exactly one vertex in each Gyj that corresponds to a valuation μ1,μ2,,μb of variables y1,y2,,yb. We claim that the combined valuation (xi=νi, yj=μj) satisfies all clauses C1,C2,,Cc, which gives a contradiction. As clique Q also has exactly one vertex in each GCk, let u be that vertex and observe that y is ugly or Y-good in GCk. If u is the ugly vertex in GCk, it means that the bad vertices are not removed, and Ck is satisfied by one of the variables x1,x2,,xa. If u is a Y-good vertex, then u corresponds to an occurrence of some variable yj. The construction of G guarantees that there is only one edge between u and a vertex in Gyj that corresponds to a valuation of yj that satisfies clause Ck. Thus, every clause is satisfied by some variable. A contradiction.

() Let X be a subset of vertices of G witch |X|ac+3c and GX does not contain Kt as a subgraph. We construct a valuation ν1,ν2,,νa of variables x1,x2,,xa such that formula

y1,y2,,yb:C1C2Cc

is false. To remove all cliques of type A, X must include at least a positive or a negative vertices in each Gxi. To remove all cliques of type B, X must include at least 3 good or 3 bad vertices in each GCk. As |X|ac+3c, we get that X includes exactly a positive or exactly a negative vertices in each Gxi. We set νi to be true if and only if X includes negative vertices in Gxi. Observe that if the constructed valuation of x1,x2,,xa satisfies clause Ck, then X has to include 3 good vertices in GCk, and the ugly vertex in GCk remains in GX. Otherwise, there would remain a clique of type C in GX.

Now, assuming to the contrary, let μ1,μ2,,μb be a valuation of variables y1,y2,,yb that satisfies all the clauses. We construct a clique Q in GX of size t=b+c the following way. For each 1jb, we select a positive (negative) vertex from Gyj when μj is true (false). For each 1kc, we select the ugly vertex from GCk if it is not removed. If the ugly vertex is removed, then we know that Ck is satisfied by one of the variables y1,y2,,yb, and we select the Y-good vertex in GCk that was assigned to the satisfied occurrence.

We claim that the resulting set of vertices induces a clique with b+c vertices. Indeed, vertices from different subgraphs Gyj, Gyj are always connected by an edge (step 8). All ugly vertices are connected to each other (step 9) and both vertices in every subgraph Gyj (step 7). Let u be a Y-good vertex selected from some GCk to Q. Vertex u corresponds to the occurrence of variable yj in Ck. Vertex u is connected to ugly and Y-good vertices in other GCk for kk (step 9) and both vertices in every subgraph Gyj for jj (step 6). As u corresponds to a satisfied occurrence of yj in Ck, we get that u is also connected to the vertex selected from Gyj. Thus, we have found a clique Kt in GX. A contradiction.