The Closed Hull Game and the Closed Interval Game
Abstract
Given a set of vertices in a graph , its geodesic interval is the set containing and all vertices on a shortest path between vertices of . A set is convex if . Moreover, the convex hull of is the smallest convex set containing . In 1984, Harary introduced convexity games where two players, Alice and Bob, alternately select vertices of a graph such that, if the set of already selected vertices is , the next player can only select a vertex in (closed interval game) or in (closed hull game). Normal and misère versions of these games have been studied and here, we introduced the optimization variants of them. Formally, given a graph and , Alice wins if the game ends after at most vertices have been selected and Bob wins otherwise. The corresponding problem consists of determining which player has a winning strategy.
We prove that the closed interval optimization game is PSPACE-complete in graphs with diameter and that the closed hull optimization game is NP-hard in bipartite graphs and in split graphs. On the positive side, we prove that both games can be solved in polynomial time in trees and that the closed hull optimization game can be solved in polynomial time in cobipartite graphs. We conjecture that the closed interval optimization game is NP-hard in cobipartite graphs and that the closed hull optimization game is PSPACE-complete in general graphs.
Keywords and phrases:
Combinatorial games in graphs, graph convexity, PSPACECopyright and License:
2012 ACM Subject Classification:
Mathematics of computing Graph theoryFunding:
Research supported by the CAPES-Cofecub project Ma 1004/23, by the Inria Associated Team CANOE, by the research grants ANR P-GASE and by the French government, through the EUR DS4H Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-17-EURE-0004.Editor:
John IaconoSeries and Publisher:
Leibniz International Proceedings in Informatics, Schloss Dagstuhl – Leibniz-Zentrum für Informatik
1 Introduction
Convexity has long been a topic of significant interest in mathematics. Its application to graph theory, however, is relatively recent, dating back to approximately 50 years ago. One of the earliest contributions in this direction is the 1972 paper by Erdős et al. [22], which studied convexity in the context of tournaments. According to Duchet [21], the first English-language publication on convexity in graphs was the 1981 paper “Convexity in Graphs” by Harary and Nieminen [25].
The main investigated convexity is the geodesic convexity. Let be a graph and a subset of vertices of . The interval of is the set together with every vertex outside that belongs to a shortest path between two vertices of . We say that is convex if . The convex hull, , of is the minimum convex set that contains . We say that is an interval set if and that is a hull set if . Typical questions are to compute a smallest hull set [23] or a smallest interval set [26] or a largest proper convex set [15] of a given graph.
In 1984, Frank Harary introduced the first graph convexity games in his abstract “Convexity in Graphs: Achievement and Avoidance Games” [24]. This line of research on graph convexity games continued until 2003, with the articles [12, 13, 27, 28] investigating Harary’s games in simple graph classes. Such games are two-player (Alice and Bob) impartial combinatorial games. The decision problem associated with them is to determine if Alice has a winning strategy.
Among Harary’s 1984 games, the following stand out: the Closed Hull Game (CHG) and the Closed Interval Game (CIG). In both, it is given a graph and, during the game, some vertices are selected by the players (forming the set of selected vertices) and other vertices are considered covered (according to specific rules of the game). Every selected vertex is a covered vertex, but not every covered vertex is a selected vertex. Alice and Bob play by alternately selecting uncovered vertices. In the game CHG, a vertex is covered if . In the game CIG, a vertex is covered if . Each game ends when all vertices are covered. That is, CHG ends when is a hull set and CIG ends when is an interval set. In the normal (resp. misère) variant, the last person to select a vertex wins (resp. loses).
In 2024, Araújo et al. [4] revived the research on graph convexity games by using the Sprague-Grundy Theory to obtain a polynomial time algorithm for the games CHG-normal and CIG-normal in trees. They also proved that the games CHG-normal and CHG-misère are PSPACE-complete. Other results on similar topics have been obtained in [6, 17]. The PSPACE-hardness of CIG-normal and CIG-misère are still open problems. For the investigation of partizan convexity games, we refer the reader to [5].
Another variant that has been extensively researched in the literature of games on graphs, in addition to the normal and misère variants, is the optimization variant. Here, roughly speaking, Alice wants to minimize some parameter of the game while Bob wants to maximize the same parameter or vice-versa. As an example, the game Kayles [7, 8] of obtaining an independent set of a graph was proved PSPACE-hard in the normal variant [29] in 1978, in the misère variant [14] in 2024 and in the optimization variant in [11] in 2025. Also the Domination game of obtaining a dominating set was proved PSPACE-hard in the optimization variant [9] in 2016 and in the normal and misère variants [10] in 2025.
In this paper, we focus on the optimization variant of CHG and CIG, denoted by CHG-opt and CIG-opt, in which it is also given an integer and Alice wins if the size of the set of selected vertices is at most , no matter who plays last. We often think of the total number of selected vertices as the time (or duration) of the game, measured in the total number of turns that have been played.
We prove that CHG-opt and CIG-opt are polynomial time solvable in trees and that CHG-opt is polynomial time solvable in cobipartite graphs. We also prove that CIG-opt is PSPACE-complete in graphs with diameter 4 and that CHG-opt is NP-hard in bipartite graphs and in split graphs. Unfortunately, we were unable to prove the PSPACE-hardness of CHG-opt, which is still an open problem.
For simplicity, in the remainder of this paper, CIG and CHG refer to the optimization variants, CIG-opt and CHG-opt.
2 Preliminaries
We define variants of the games CHG and CIG, denoted by and , whose inputs are a triple , where is a graph, is a positive integer, a convex subset of vertices of , and where the vertices of are considered covered (without being selected) at the beginning of the game. Therefore, the set of selected vertices is a set . In , after is selected the covered vertices are and in the covered vertices are . Alice wins if the total number of selected vertices is at most . Note that is equivalent to (and similarly for CIG).
In both games, a strategy for a player on a graph where is initially covered, is a function such that for every , we have that is the next vertex to be selected by the player when the set of selected vertices is . Note that this implies that .
A -strategy for Alice is a strategy that ensures that at the end of the game at most vertices are selected (in total) regardless of how Bob plays (recall that we do not count the vertices in ). A -strategy for Bob is one that ensures that more than vertices are selected, regardless of how Alice plays. A strategy for Alice (resp. Bob) in a graph is optimal if it is a -strategy with minimum (resp. maximum) .
Let the closed game hull number, (resp., ), be the smallest integer such that Alice has a -strategy for when Alice starts (resp., Bob starts). And let the closed game interval numbers and be defined analogously for the game . Let and . The parameters closed game hull number and closed game interval number were introduced in [4].
In the next lemma, we establish important properties of the parameters and . The main idea of the proof for the first part of this lemma is to build a strategy where one “plays ignoring a given set of vertices”.
Lemma 1.
Let be a graph and where and are convex sets. Then,
-
(a)
,
-
(b)
and both bounds are tight.
Lemma 2.
Let be a connected graph. If there exists a -strategy for Bob in the game CHG, then there exists one such strategy such that is a neighbor of in , for every convex set .
Proof.
Let be a -strategy for Bob in the game CHG. Suppose that is a convex set with and define . Since and is connected, there exists that lies on a shortest path from to some vertex in . By Lemma 1, since , we have . This inequality guarantees that if Bob selects instead of , Alice (in her turn) will not be able to make the game finish in a shorter time (if Bob plays optimally). That is, there is a strategy for Bob in which that is not worse than . Such is a -strategy for Bob. Repeating this argument, over the course of any game, one can see that Bob can always decide to play at a neighbor of the set of currently covered vertices. Therefore, there exists a strategy as desired.
Note that, if the graph is not connected, we can prove similarly that, in the game CHG, either Bob plays a neighbor of a covered vertex or the first selected vertex of a component.
3 Hardness results
In this section, first we prove that the Closed Interval Game (optimization variant) is Pspace-complete; later we show that Closed Hull Game (optimization variant) is NP-hard in bipartite graphs and in split graphs.
3.1 CIG is PSPACE-complete
We start with a (a priori weaker) statement: the extension CIG∗ (defined in section 2) is Pspace-complete. Later we use this result to prove that CIG is also Pspace-complete.
Our reduction is from a variant of the game Kayles-max, which is the optimization version of the classical Kayles game. In Kayles-max, the instance is a graph and an integer and two players, Max and Min (starting with Max), alternate turns selecting vertices of in such a way that the set of selected vertices must always induce an independent set. Max wins if the number of selected vertices is at least at the end of the game. Otherwise, Min wins.
Kayles-max was proved Pspace-complete by Brosse et al. [11] in 2025. One can easily check that the instances constructed in the reduction of [11] have the important property of pass-stability. We say that an instance of a game is pass-stable if the player with a winning strategy also wins if the players are allowed to pass moves. Note that the notion of pass-stability is defined according to a player that has a winning strategy. Hence, the winning player will never pass and so the game is still finite and well defined even if the opponent is allowed to pass. In some games, every instance is pass-stable, such as the PosCnf game. Other games have instances that are not pass-stable, such as the Graph Coloring Game, which was also proved Pspace-complete even for pass-stable instances [16].
We first prove in the theorem below that the variant Kayles-min, in which Min is the first to play, is also Pspace-complete even for pass-stable instances.
Theorem 3.
Kayles-min is Pspace-complete even restricted to pass-stable instances with graphs of even order.
In the following, we use Kayles-min to prove that CIG∗ is Pspace-complete.
Theorem 4.
CIG∗ is Pspace-complete even restricted to pass-stable instances such that is a multiple of , has diameter 3 and is a clique of vertices satisfying that every vertex outside has a neighbor in . The same is valid for the variant of CIG∗ in which Bob plays first. In other words, and are Pspace-hard even when has diameter 3 and is a clique with vertices of , where .
Now we prove that the game CIG is Pspace-complete. We obtain a reduction from CIG∗, which we proved Pspace-complete in Theorem 4 even for pass-stable instances.
Theorem 5.
The Closed Interval Game CIG (optimization variant) is Pspace-complete even for pass-stable instances with diameter at most 4. The same is valid also for the variant in which Bob plays first. In other words, and are Pspace-hard even in graphs with diameter 4.
Proof.
Due to Theorem 4, we can start considering a pass-stable instance of CIG∗ such that is a graph with vertices, is a clique of with vertices (of pre-selected vertices) such that every vertex outside has a neighbor in and is an integer. Let .
Let us construct an instance for CIG, which we will prove that is pass-stable and will be used to solve CIG∗ for . The graph is obtained from by including, for every vertex , the gadget of Figure 1, with , where is an independent set with vertices and is such that both and dominate . Notice that does not contain and that is a cut vertex in , that is, the removal of disconnects the gadget from the rest of the graph . Also set .
Claim 6.
If Alice is the first to play in (i.e., to select a vertex from ), then she can ensure the selection of at most vertices of . If Bob is the first to play in , then he can ensure the selection of at least vertices of .
Proof.
First suppose that Alice is the first to play in . Then she can select . If Bob ever plays in , she plays in , ensuring that no other vertex from can be selected during the rest of the game, summing at most 3 selected vertices in . If Bob plays in , no other vertex of can be selected.
Now suppose that Bob is the first to play in . Then he can select . If Alice plays in , Bob plays (without loss of generality) in , ensuring that at least one of and cannot be selected. Since every vertex of belongs to only one shortest path of and this path is between and , then every vertex of will eventually have to be selected during the game, ensuring at least selected vertices in .
Therefore, if Alice is the first to play in more than half of the gadgets , she ensures the selection of at most
Moreover, if Bob is the first to play in more than half of the gadgets , he ensures the selection of at least
We prove below that both Alice and Bob, in order not to lose the game, have to be the first to play in exactly half of the gadgets , and each gadget controlled by Alice has to have exactly 3 selected vertices and each gadget controlled by Bob has to have exactly selected vertices, summing exactly
Since , we have that . Thus, if a player is the first to play in more than half of the gadgets , they win.
So, suppose that a player wins CIG∗ on . Let be the opponent of . We say that a vertex of a gadget is good if either it belongs to and is Alice or it belongs to and is Bob. Otherwise, we say that is bad.
Consider the following strategy for on CIG on the graph with integer . We divide the strategy in two Phases, (i) and (ii), described below. During the game, we say that a gadget is an Alice’s gadget if the number of selected vertices in is greater than in . Analogously for a Bob’s gadget. If the number of selected vertices in is equal to in , we say that the gadget has no owner, which can occur when both and have 0 or 1 selected vertex. We say that the game is in Phase (i) until every gadget has an owner; after that, we say that the game is in Phase (ii).
If is Alice, she first selects at the beginning of the game, in Phase (i).
-
(i1)
If the player selects a bad vertex of a ’s gadget (no bad vertex of was selected before and there is exactly one good vertex selected in ), then selects other good vertex of , making a ’s gadget again;
-
(i2)
If the player selects a good vertex of a gadget in which no other good vertex was selected, then selects other good vertex of ;
-
(i3)
If is Bob and (Alice) selects a bad vertex () of a gadget in which no vertex was selected before, then (Bob) selects a good vertex () of ;
-
(i4)
Otherwise, if there is a gadget without selected vertices, then selects a good vertex of .
From the discussed before, we may assume that the number of Alice’s gadgets will be half of the gadgets. The same for Bob’s gadgets. To achieve this, at most one vertex of is selected during Phase (i) and, if a vertex of was selected, it was by =Alice (from ’s strategy above). Moreover, we may assume without loss of generality that (resp. ) is selected for any Alice’s (resp. Bob’s) gadget . Consider now that we are in Phase (ii): every gadget has an owner. Therefore, every vertex is forbidden, because it is in a shortest path between two selected vertices of different gadgets.
If is Alice, she first selects a vertex of following her winning strategy in CIG∗ at the beginning of Phase (ii). After that:
-
(ii1)
If the player selects either a vertex of , or a good vertex of a ’s gadget , or a bad vertex of a ’s gadget , then selects a vertex of following the winning strategy in CIG∗ (recall that the graph is pass-stable in the game CIG∗);
-
(ii2)
If the player selects a bad vertex of a ’s gadget (with exactly one good vertex selected), then selects other good vertex of , making a ’s gadget again;
-
(ii3)
If the player selects a good vertex of a ’s gadget , then selects other good vertex of , making it a ’s gadget and winning the game.
With this, notice that, since every shortest path between a vertex of and a vertex outside passes through , the game CIG on works similarly to the game CIG∗ on with already selected. Moreover, the player plays in according to the winning strategy on CIG∗, ensuring at most (resp. more than) selected vertices in if is Alice (resp. Bob). Furthermore, if is Alice, she ensures at most 3 selected vertices in each Alice’s gadget and at most selected vertices in each Bob’s gadget, summing at most selected vertices in . Finally, if is Bob, he ensures at least 3 selected vertices in each Alice’s gadget and at least selected vertices in each Bob’s gadget, summing more than selected vertices in .
3.2 CHG is NP-hard
The Hull Number problem takes a graph and as inputs and aims at deciding whether , where is the minimum size of a hull set of .
Theorem 7 ([1]).
The Hull Number problem is NP-complete in the class of bipartite graphs.
Here we prove the following theorem about the closed game hull number.
Theorem 8.
Deciding whether holds is NP-hard in the class of bipartite graphs.
Sketch of proof.
Let be a bipartite instance of the Hull Number problem and let be the bipartite graph obtained by subdividing all edges of twice. That is, every edge is replaced by a path . Note that, for every (called original vertices), . Therefore, for every , every shortest -path in corresponds to one shortest -path in . Moreover, for every (where is the neighbor of in the path resulting from the subdivision of ) and every and every shortest -path in : either and is a shortest -path in , or (adding to ) is a shortest -path in . Therefore, for every hull set of , there exists a hull set of with (indeed, for every , remove and add if ). Reciprocally, for every hull set of , is also a hull set of . Hence, and there exists a minimum hull set of using only original vertices (i.e., ).
Next, we shall prove that and so prove the NP-hardness.
Theorem 9.
Deciding whether (resp. ) is NP-hard in the class of split graphs.
Sketch of proof.
Let be an instance of the classical problem of Minimum Dominating Set, which is NP-complete and not approximable up to factor [18]. Let .
Let be obtained from as follows. Let be a stable set of order . Let be a clique on vertices: . For any , make adjacent to if and only if or . Clearly is a split graph. Note that, for any , are true twins in (vertices with same closed neighborhood). Also note that the vertices of are simplicial in (and so belong to any interval/hull set) and that the (unique) minimum hull set (resp., interval set) of is .
4 Polynomial algorithms in trees and cobipartite graphs
4.1 CHG and CIG are polynomial in Trees
In this section, we show that the games CHG and CIG are polynomial time solvable in trees.
Let be a tree with at least 3 vertices. We will denote by its set of leaves. Note that for every , there is no path in for which is an internal vertex. So all leaves of will have to be selected by either Alice or Bob before the game ends. A vertex is internal (of the tree) if it is not a leaf. We first prove the following two technical lemmas.
Lemma 10.
Let be a tree. If there exists a -strategy for Alice, then there exists a -strategy for Alice such that she only selects leaves.
Lemma 11.
Let be a tree, be a non-empty convex set such that contains at least one internal vertex. If Alice has a -strategy in , then she has a -strategy first selecting a leaf whose only neighbor is not covered (not in ).
The eccentricity of a vertex is the maximum distance from to the other vertices. The radius of a graph is the minimum eccentricity among all vertices of . The center of a graph consists of the vertices with minimum eccentricity. It is known that the center of a tree is either one vertex or two adjacent vertices.
Theorem 12.
Let be a tree. If or has radius 1, then . Otherwise,
where is the tree obtained from by removing its leaves. Thus, and can be computed in polynomial time. Moreover, if Bob is allowed to pass turns, it cannot increase the length (the number of vertices actually selected by the players) of the game.
Proof.
The proof is by induction on the radius of . If , then clearly . If has radius 1 and at least 3 vertices, then is a star and Bob can always select the only internal vertex of and then . Moreover, in these cases, if Bob can pass turns, this cannot increase the length of the game.
So, assume that has at least 4 vertices and radius and let . Note that the radius and, by induction, let be an optimal (i.e., finishing in turns) strategy for Alice, playing only in the leaves of . Also, assume by induction that, following , the number of turns cannot increase if Bob can pass turns.
Alice’s strategy.
We first describe an Alice’s strategy when she is first to play in . Let be the leaf of first selected by Alice when playing in . Then Alice selects one leaf of adjacent to . From now on, let be the vertex selected by Bob just after Alice has selected her -th vertex in the game on (unless if Bob passes his turn). From this, Alice will assume that Bob played on a certain vertex on the game in described below or passed his turn.
If is a leaf of and its neighbor in is already covered but there is a vertex not covered on , or if Bob passes his turn, Alice assumes that Bob passed his turn in the game on . That is, let be the leaf of selected by Alice following assuming that Bob has passed his last turn. Then, in , Alice selects any leaf of adjacent to .
If is a leaf of and its neighbor in is not already covered, let . Moreover, if is not a leaf of , let . Then, let be the leaf of selected by Alice in her optimal strategy in , considering that Bob played on at its -th turn. Then Alice plays on a leaf of adjacent to in the game on . Finally, if is a leaf of and all vertices of are already covered, then Alice will select any leaf of .
Note that, while all vertices of are not covered, each move of Alice following covers exactly the same vertices of as when she follows in . Hence, all vertices of are covered after at most steps, i.e., after Alice has selected leaves of and Bob has selected vertices (where is the number of turns Bob passed). Once all vertices of have been covered, it remains at most leaves of that are not covered (since Alice has selected leaves and Bob might have selected ones too). Hence, if Alice follows , the game ends in at most steps.
Bob’s strategy.
Now we show a Bob’s strategy with at least vertices in the game in which Alice is first to play, given Bob’s best strategy in . Recall from Lemma 10 that we may assume that Alice only plays on leaves and that, by Lemma 11 that, unless all internal vertices (i.e., of ) are covered, she selects a leaf whose neighbor is not covered. Let be the leaf of selected by Alice in her -th turn of the game on and let be the leaf of adjacent to . If all vertices of are already covered (in the game in ), Bob can only select leaves of . Otherwise, we may assume that was not already covered by remark above (by Lemma 11). Since was not selected before (considering the game on ), Bob assumes that Alice played on in the game on . From this, let be the vertex of in which Bob would play in his best strategy on after Alice selects . Then Bob plays on in the game on . Notice that, at the end of the game, Bob selected at least vertices of in the game on , and we are done (since the number of steps of the game equals the number of selected internal vertices plus the number of leaves of ). Analogously for .
4.2 CHG is polynomial in Cobipartite graphs
A graph is a cobipartite graph (complement of a bipartite graph) if there is a partition such that and are cliques. Denote by the set of simplicial vertices of . Let and . Let be the set of universal vertices of . Note that if is not a complete graph, then . Let be the graph obtained from by removing the vertices in and , and removing the edges intra-cliques, i.e., and . Denote by with the set of connected components of . Note that if is neither one complete graph nor the disjoint union of complete graphs, is not empty, and every component has at least two vertices, for every . Indeed, any vertex in (resp., in ) has a neighbor in (resp., in ).
In the following, we will use the results presented by Araujo et al. [1] for the hull number in cobipartite graphs. In particular, to show that computing can be done in polynomial time in the class of cobipartite graphs , they precisely characterized minimum hull sets of such graphs. We recall their result:
Theorem 13 ([1]).
Let be a cobipartite graph (with the notations defined above). Recall that any hull set must contain . Furthermore:
-
If and and then is a minimum hull set of .
-
Otherwise (if or or ):
-
–
If , let for and , then is a minimum hull set of .
-
–
Otherwise (if ), let be the unique component of .
-
*
If and (and so ), then is a minimum hull set for every .
-
*
If and , then . Precisely, let be such that is maximum and let , then is a hull set of .
-
*
If and , then .
-
*
-
–
We first specify one of the cases in the above theorem.
Lemma 14.
Let be a cobipartite graph that is not a complete graph. If and and , then .
We are now ready to prove the main result of this section.
Theorem 15.
The (resp., ) can be computed in polynomial time in the class of cobipartite graphs .
Sketch of proof.
We prove the theorem in the case of , the case is similar. The proof follows the characterization of Theorem 13 (each of the following claims corresponds to one case of Theorem 13).
Note that if is a complete graph or a disjoint union of complete graphs, then . From now on, let be a cobipartite graph that is neither a complete graph nor the disjoint union of complete graphs. We prove the following.
-
Let be a cobipartite graph other than a complete graph and the disjoint union of complete graphs and , and . Then, .
-
Let be a cobipartite graph other than a complete graph. If or , and , then .
-
Let be a cobipartite graph, not a complete graph or the disjoint union of complete graphs. If , and and , then .
-
Let be a cobipartite graph, not a complete graph or the disjoint union of complete graphs. If and and , then .
-
Let be a cobipartite graph with . If and , then can be computed in polynomial time. In particular, if , .
5 Conclusion
The main remaining open question is whether the closed hull game, CHG, (optimization variant) is PSPACE-complete, which we conjecture to be true. It is also interesting to find other graph classes in which the decision problems of the games CHG and CIG (optimization variants) are polynomial time solvable. Unfortunately, it seems that the closed interval game CIG is hard on cobipartite graphs as the following argument suggests. First we prove that deciding if a cobipartite graph admits an interval set of size is NP-complete (to the best of our knowledge, this was only proved for oriented graphs [3]).
Theorem 16.
The problem of deciding if a cobipartite graph has an interval set (in the geodesic or convexities) with at most vertices is NP-complete.
The above reduction implies that, in the closed interval game CIG, Alice must ensure the selection of a dominating set of , which also must be an independent set (since the game is closed). In other words, she aims at obtaining an independent set of as small as possible. That is, Alice and Bob are playing the optimization variant of the Kayles game on where the first player aims at minimizing the obtained independent set. This problem is PSPACE-complete by Theorem 3. However, since is bipartite, to prove that the closed interval game is PSPACE-complete in cobipartite graphs, we would need to show that the optimization variant of the Kayles game starting with Bob is PSPACE-complete in bipartite graphs, which is still an open question.
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