Computing the hull number in toll convexity
Mitre C. Dourado

TL;DR
This paper introduces a polynomial-time algorithm to compute the toll hull number in general graphs, addressing a specific convexity measure based on tolled walks.
Contribution
It presents the first polynomial-time algorithm for determining the toll hull number in any graph, advancing understanding of toll convexity.
Findings
Algorithm efficiently computes toll hull number
Polynomial-time complexity achieved for general graphs
Enhances analysis of toll convexity in graph theory
Abstract
A walk between vertices and of a graph is called a {\em tolled walk between and } if , as well as , has exactly one neighbour in . A set is {\em toll convex} if the vertices contained in any tolled walk between two vertices of are contained in . The {\em toll convex hull of } is the minimum toll convex set containing~. The {\em toll hull number of } is the minimum cardinality of a set such that the toll convex hull of is . The main contribution of this work is an algorithm for computing the toll hull number of a general graph in polynomial time.
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Taxonomy
TopicsAdvanced Graph Theory Research · Graph Labeling and Dimension Problems · Limits and Structures in Graph Theory
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11institutetext: Mitre C. Dourado 22institutetext: Instituto de Matemática,
Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
22email: [email protected]
Computing the hull number in toll convexity††thanks: Partially supported by CNPq, Brazil.
Mitre C. Dourado
(Received: date / Accepted: date)
Abstract
A tolled walk between vertices and in a graph is a walk in which is adjacent only to the second vertex of and is adjacent only to the second-to-last vertex of . A set is toll convex if the vertices contained in any tolled walk between two vertices of are contained in . The toll convex hull of is the minimum toll convex set containing . The toll hull number of is the minimum cardinality of a set such that the toll convex hull of is . The main contribution of this work is a polynomial-time algorithm for computing the toll hull number of a general graph.
Keywords:
Extreme vertex hull number minimum toll hull sets toll convexity
MSC:
05C85
1 Introduction
A family of subsets of a finite set is a convexity on if and is closed under intersection van de Vel (1993). Graph convexities have gained attention in the last decades Duchet (1988); Gimbel (2003); Henning et al. (2013); Pelayo (2013). Unlike the most studied graphs convexities, which are defined by a family of paths, the toll convexity Alcón et al. (2015); Gologranc and Repolusk (2017) uses a special kind of walks. Recall that a walk between vertices and of a graph (or a -walk) is a sequence of vertices such that , , , and for . A tolled walk between vertices and (or a tolled -walk) is a walk such that
- •
,
- •
implies , and
- •
implies .
Given a graph , a set is toll convex (or t-convex) if the vertices contained in any tolled walk between two vertices of are contained in ; and is toll concave (or t-concave) if is t-convex. The toll interval of is belongs to some tolled -walk. The toll interval of is if and otherwise. If , then is said to be a toll interval set of and the minimum cardinality of a toll interval set of is the toll number of . The toll convex hull of , denoted by , is the minimum t-convex set containing . If , then is said to be a toll hull set of and the minimum cardinality of a toll hull set of is the toll hull number of . The main contribution of this work is a polynomial-time algorithm for computing the toll hull number of a general graph.
In the well-known geodetic convexity Farber and Jamison (1986); Pelayo (2013), monophonic convexity Duchet (1988); Edelman and Jamison (1985), and * convexity* Dourado et al. (2012); Henning et al. (2013) all above concepts are analogously defined by replacing “tolled walk” by “shortest path”, “induced path”, and “path of order three”, respectively. Note that every shortest path is an induced path, and every induced path is a tolled walk. This implies that the toll convex hull of contains the monophonic convex hull of and consequently also contains the geodetic convex hull of . Therefore, the toll hull number is a lower bound of the monophonic and the geodetic hull numbers of the graph.
In the geodetic convexity, determining whether the hull number is at most is -hard for general graphs Coelho et al. (2015), -complete for partial cube graphs Albenque and Knauer (2016) and chordal graphs Bessey et al. (2018), and solvable in polynomial time for unit interval graphs, cographs, split graphs Dourado et al. (2009), cactus graphs, -sparse graphs Araujo et al. (2013), distance hereditary graphs Kante and Nourine (2016), (,triangle)-free graphs Araujo et al. (2016). In the convexity, this problem is -hard even for bipartite graphs with maximum degree Coelho et al. (2015), and can be solved in polynomial time for block graphs and chordal graphs Centeno et al. (2011). However, the monophonic hull number can be computed in polynomial time for general graphs Dourado et al. (2010). In the toll convexity, it is known that the hull number of every tree different of a caterpillar is equal to 2 Alcón et al. (2015).
A graph is an interval graph if every vertex of can be associated with an interval of a straight line such that two vertices of are neighbors if and only if the corresponding intervals intersect. Given a convexity on the vertex set of , we say that is a convex geometry under if every -convex set of is equal to the -convex hull of its -extreme vertices. In Alcón et al. (2015), it was shown that the interval graphs are precisely the graphs which are convex geometries in the toll convexity. They also characterized the t-convex sets of a general graph and of some graph products. In Gologranc and Repolusk (2017), the toll number of the Cartesian and the lexicographic product of graphs are studied, where some characterizations are presented.
The text is organized as follows. In Section 2, we present useful definitions, notations, and results. We also present the notion of hull characteristic family, which plays an important role in the proposed algorithm and can be an useful tool for future works dealing with the hull number. Section 3 contains the algorithm for computing the toll hull number of a general graph in polynomial time. In the conclusions, we discuss that this result leads to an algorithm for generating all minimum toll hull sets of a general graph with polynomial delay and to a characterization of the toll extreme vertices of a graph.
2 Useful tools
We consider finite, simple, and undirected graphs. For a graph , its vertex and edge sets are denoted by and , for a vertex , the open and the closed neighborhoods of are denoted by and , respectively. Vertices are twins if . For , the neighborhood of is , is , and . We will also use . We can also use -path to refer to a path between vertices and .
Denote by the graph obtained by the deletion of the vertices of ; and by the subgraph of induced by . If every two vertices of are adjacent, then is a clique of . For a set , we say that is complete if every vertex of is adjacent to every vertex of . Vertex is simplicial in if is a clique. If is a clique, then is said to be a complete graph.
We say that separates vertices if there is a -path in but there is no one in ; that is a separator of if separates some pair of vertices of ; and that is a clique separator of if is a clique and a separator of . We say that is reducible if it contains a clique separator, otherwise it is prime. A maximal prime subgraph of (or mp-subgraph of ) is a maximal induced subgraph of that is prime. An mp-subgraph of a reducible graph is called extremal if there is an mp-subgraph different of such that for every mp-subgraph different of , it holds . See Figure 1 for an example.
The following result on the monophonic convexity solves the problem of determining the toll hull number when the input graph is prime.
Theorem 2.1
Dourado et al. (2010)* If is a prime graph that is not a complete graph, then every pair of non-adjacent vertices is a monophonic hull set of .*
Corollary 1
Let be a prime graph. If is a complete graph, then the toll hull number of is ; otherwise every two non-adjacent vertices form a toll hull set of .
Proof
If is a complete graph, it is clear that is the only toll hull set of . If is a not a complete graph, then the result follows from Theorem 2.1 because the monophonic convex hull of is contained in the toll convex hull of for any set .
The following results state useful properties of reducible graphs.
Lemma 1
Leimer (1993)* Every reducible graph has at least two extremal mp-subgraphs.*
Given a tolled -walk , observe that if , then ; and that if , then . A vertex of a t-convex set is extreme in if is also a t-convex set. Denote the set of toll extreme vertices of by . It is clear that is subset of every toll interval set and of every toll hull set of and that every toll extreme vertex is a simplicial vertex but the converse is not always true.
Lemma 2
If is a subgraph of a graph such that every mp-subgraph of is also an mp-subgraph of , then every induced -path for contains at least one internal vertex of if .
Proof
If , , and is an induced -path whose none internal vertex belongs to , then the union of and is a prime subgraph of properly containing an mp-subgraph of , which is a contradiction.
We observe that is a clique for every t-concave set that induces a connected graph, because if are non-adjacent, then any induced -path of is a tolled -walk containing vertices of .
Lemma 3
Alcón et al. (2015)* A vertex is in some tolled walk between non-adjacent vertices and if and only if does not separate from and does not separate from .*
Lemma 3 can be used to test whether a set is t-concave as follows. In fact, we show how to compute the toll interval of and in polynomial time, which also allows one to test whether a vertex is toll extreme, which is equivalent to consider , and to compute the toll convex hull of in polynomial time. For every and such , if and belong to the same connected component of and and belong to the same connected component of , then . This can be done in steps, where is the number of vertices and the number of edges.
The following result contains useful properties of tolled walks.
Lemma 4
Let be a graph, let , let be maximal such that is connected, and let . The following sentences are equivalent.
There is a tolled -walk containing vertices of . 2.
There is a tolled -walk containing vertices such that , , , and . 3.
.
Proof
Let be a tolled -walk containing vertex . Since separates from and is maximal contained in with connected, we can write such that and . Therefore, , , and, by the definition of tolled walk, and .
Let be a tolled -walk containing vertices such that , , , and . Let and let . Since is connected, there is a -walk in containing all vertices of . (Note that is not necessarily a tolled walk.) It is easy to see that the concatenation of and , where is a subwalk of from to and is a subwalk of from to , is a tolled -walk containing all vertices of .
Direct from the definitions.
We conclude this section introducing the hull characteristic families.
If is a concave set of a convexity on a set , then every hull set of contains at least one vertex of . We define the granularity of under as the maximum integer such that every hull set of has at least vertices of . Let be a family of pairwise disjoint concave sets of . The granularity of is the sum of the granularities of its members. We say that is a hull characteristic family of if the hull number of is equal to the granularity of .
The problem of computing the hull number of can be reduced to the one of finding a hull characteristic family of and computing the granularity of each of its members. The family formed only by is itself a trivial hull characteristic family of , but it brings no advantage of the use of this notion for determining the hull number of . The number of hull characteristic families of can be an exponential on the size of . For instance, every partition of the vertex set , where is a complete graph, is a hull characteristic family of the toll convexity of , since the toll hull number of is when is a complete graph. An example of a non-trivial hull characteristic family in toll convexity is the family of vertices of the graph of Figure 1. One can use Lemma 3 to see that the members of are really t-concave sets. In fact, this lemma can be used to show that all vertices of are extreme vertices, then . Since is not a toll hull set of , the toll hull number of is at least 4. Now, considering the tolled walks and , we conclude that is a toll hull set of , that , and also that the toll hull number of is .
3 The algorithm
The central idea of the proposed algorithm is to find a toll hull characteristic family of the input graph such that the granularity of each member of can be determined in polynomial time. In order to get this, the algorithm begins constructing families and of vertex sets such that for any . During the algorithm, the members of such that is not t-concave can be joined with other members of so that, at the end, the sets that are t-concave sets form the desired family. The following classification of the t-concave sets of a graph is useful to accomplish this task.
[TABLE]
Lemma 5
If is a t-concave set of a graph , then if the type of is and if the type of is .
Proof
The case is trivial. For , suppose for contradiction that is a toll hull set of such that . The type of implies . Since is t-concave and there is, for some , a tolled -walk containing some vertex . However, since , separates from , which contradicts Lemma 3.
Finally consider . We claim that all vertices of are toll extreme vertices, which implies that . Suppose the contrary and let be a tolled -walk containing some vertex . Since is t-concave, at least one, say , belongs to . Since is a clique, . Now, the fact that separates from contradicts Lemma 3.
An example of a t-concave set with granularity strictly bigger than its type is the set of Figure 1, since the type of is and because vertices are toll extreme vertices of the graph.
Once a t-concave set is formed by the algorithm, it can be chosen in a later iteration to compose another t-concave set. The vertices of that will be chosen to constitute the minimum toll hull set returned by the algorithm depends of the type of and of other properties that has at the moment that is formed. They are detailed in the following numbered choices. The first 3 choices are for t-concave sets of type 1.
Choice 1
add to a vertex having a non-neighbor in
Choice 2
add to a vertex having a non-neighbor in and a non-neighbor in , and there are different such that and .
Choice 3
add to a vertex having a non-neighbor in if there are different such that and .
The remaining 5 choices are for t-concave sets of type 2.
Choice 4
add to non-adjacent vertices .
Choice 5
add to non-adjacent vertices for some both having non-neighbors , respectively, such that does not separate from , and does not separate from .
Choice 6
for , add to a vertex for some having a non-neighbor such that , does not separate from , and does not separate from .
Choice 7
add to a vertex for some having a non-neighbor in where is t-concave of type .
Choice 8
add to a vertex for some where is t-concave of type .
Lemma 6
The following sentences hold for and chosen at the same time from in Algorithm 1.
If , then . 2.
. 3.
* is connected for every clique .* 4.
* is a clique.* 5.
If for every , then is disconnected.
Proof
and The mp-subgraphs of are obtained in line 1. The extremal mp-subgraphs of form family (line 1) and the remaining ones form family (line 1). Note that for any mp-subgraph of , is the only mp-subgraph containing the vertices of . Since the definition of extremal mp-subgraph implies that for every extremal mp-subgraph of , and hold when line 1 finishes.
The only operations performed on are deletions and on are deletions and inclusions. Since deletions do not interfere in the properties of items and , we only need to consider the inclusions on . The only line adding members to after line 1 is line 1, which is inside of the while loop.
A set constructed in line 1 contains the set chosen in line 1 of the same iteration. Therefore, every member of at the end of iteration contains the vertex set of at least one extremal mp-subgraph of , which means that is non-empty and does hold.
Now, note that every member belonging to at the end of iteration of the while loop is present in at the beginning of iteration , except the new one, namely, the set constructed in line 1. Therefore, if there are two sets such that at the end of iteration and is minimum with this property, them one of them is , say . However, observe that and do not change from the beginning to the end of iteration , which implies that , a contradiction. Then does hold.
Suppose that is disconnected. If is an mp-subgraph of , then it is clear that is connected. Therefore, we can choose as the first set added to in line 1 such that is disconnected. Write . Note that is not equal to any mp-subgraph of forming and that there is an mp-subgraph of composing which contains . Furthermore, since is connected for every mp-subgraph of , there are mp-subgraphs and of doing part of such that and belongs to connected component of different of the one containing . Hence, the set , chosen in line 1 of some iteration of the while loop, has the property that . Therefore, the member of containing should have been chosen to compose the set , constructed in line 1 of iteration , which is a subset of , yielding a contradiction because every mp-subgraph of belongs to exactly one member of during all the algorithm.
Since the intersection of two mp-subgraphs is a clique, we can consider that is the number of the least iteration of the while loop that finishes with having members and whose intersection is not a clique. Without loss of generality, we can assume that , where is the set added to in line 1 of iteration . Observe that the intersection of and every member of of iteration is a clique. Therefore, intersects with at least two members and there are and . We know that there is a vertex because otherwise would have been chosen to be part of . Let be an induced -path of and let be an induced -path of . These paths exist because guarantee that and are connected graphs once we know that the intersection of with any member of f of iteration is a clique. Now, from the concatenation of and we can obtain an induced -path whose internal vertices do not belong to , which contradicts the hypothesis that is composed by mp-subgraphs.
Let be such that is not contained in any other member of and let be an mp-subgraph of not composing such that is maximal. Therefore, there is an mp-subgraph of not composing such that satisfies . By the maximality of , there is . Choose for . Vertex exists because is an mp-subgraph for .
First, we show that is a clique. Suppose the contrary. By , there are and such that . Therefore, if is connected, since both and have neighbors in , there is an induced -path whose internal vertices do not belong to , which contradicts Lemma 2.
Next, we show that . Suppose the contrary and let . By the choice of and and the fact that is a clique, has a non-neighbor . Assume that . (The case where is analogue.) Therefore, using and , we can find an induced -path whose internal vertices do not belong to , which contradicts the assumption that is an mp-subgraph once .
Now, supposing that is connected and let be a -path of , we reach in a contradiction because the union of , , and is a prime subgraph of properly containing an mp-subgraph.
Corollary 2
If is a non-extremal mp-subgraph of , then is disconnected.
Proof
It is a special case of Lemma 6 when is a non-extremal mp-subgraph.
Lemma 7
The following sentences hold at any time of Algorithm 1 for if is t-concave.
* is contained in an mp-subgraph not composing .* 2.
* is connected.* 3.
* is a clique.* 4.
.
Proof
Suppose by contradiction that is not contained in a mp-subgraph of not composing . Then, there are mp-subgraphs and of not composing such that there are and . By the definition of mp-subgraph, we can choose and such that . Let be an induced -path of and let be an induced -path of . Note that and are cliques. By Lemma 6 , and are connected subgraphs of . Lemma 6 guarantees that there is a vertex . Since belongs to and to , there is an induced -path in and there is an induced -path in . Since the concatenation of the paths , and is a tolled -walk containing , we have a contradiction.
Suppose by contradiction that is disconnected. If was added to in line 1, then is connected because is an extremal mp-subgraph of . Then, we can choose as the first set added to in line 1 such that is t-concave and is disconnected. Therefore, is equal to a set constructed in line 1 of some iteration . Let and be connected components of . Hence, and belong to different members and of constructed in iteration . Therefore, for the set chosen in line 1 of iteration , it holds that is contained in and in . On the one hand, contains . Since item guarantees that there is an mp-subgraph not composing containing , there is a member of that should have been chosen to compose in iteration that was not chosen, which is a contradiction. On the other hand, there is a vertex of present in and in , but this is a contradiction because different connected components have disjoint vertex sets.
It is a consequence of item and Lemma 6 .
By the definitions, it holds for any set . Now, if and is t-concave, by item , there are mp-subgraphs and properly containing such that and . If some vertex had no neighbor in , then would be a clique separator of . Then .
Whenever Algorithm 1 constructs a t-concave set , some vertices of are choosen appropriatly to form the toll hull set that will be returned at the end. We show in the next result that at least one choice of each line is always possible.
Lemma 8
In Algorithm 1, Choices and 8 are always possible in lines and 1, respectively, and Choice 5 or 6 is possible in line 1.
Proof
Since it is easy to see that Choice 1 is always possible for a t-concave set having type 1 and Choices 4 and 8 are always possible for a t-concave set having type 2, we discuss the choices of lines 1 and 1 separatedly in the sequel.
Now, we show that Choice 3 is possible in line 1. By definition of type 1, there is with a non-neighbor in . On the one hand . By Lemma 6 , is a clique. Let be a set containing . If , then there is such that . Using Lemma 6 again, we have that is a clique containing , which would imply that is adjacent to all vertices of . Then, . Choose any other member of as and Choice 3 is possible for this case. On the other hand . Then, there is one member of not containing , call it . Any vertex of can be chosen as . Choose as any member containing . It there exists because is a clique. Then, Choice 3 is possible in line 1.
Finally, we show that Choice 5 or 6 is possible in line 1. Since is not t-concave and is a clique, there are vertices for which there is a tolled -walk containing vertices of . Since is t-concave of type 2, is complete, which implies that both belong to . If there is such that , we have Choice 5 because if for some , then is adjacent to all vertices of or . The other possibility is and for , which matches with Choice 6.
The following result guarantees that if the constructed in line 1 of Algorithm 1 is such that is t-concave, then at most two sets used to compose are such that is t-concave. Furthermore, the type of is 1.
Lemma 9
Let and be obtained in lines 1 and 1 of some iteration of the while loop of Algorithm 1, respectively. If is t-concave, then has at most two sets such that is t-concave and the type of is .
Proof
Let be the set chosen in line 1 of iteration and let be such that there is a tolled -walk containing some vertex of . Denote by the family formed by the sets such that is t-concave. First, suppose by contradiction that . Since is t-concave, Lemma 7 implies that is a clique. Since , we conclude that is a clique, which means that that .
By Lemma 6 , the sets and are pairwise disjoint. Hence, for at least one of them, say , it holds . Furthermore, we have that for because each of and has at least one non-neighbour in , for , and is a clique. Note that has different vertices such that and . By Lemma 7 , every vertex of has at least one neighbor in . Since and is connected by Lemma 7 , Lemma 4 implies that , which contradicts the assumption that is t-concave. Therefore, .
Now, suppose by contradiction that is a t-concave set of type 2 or 3. Hence is complete, which implies that because each of and has at least one non-neighbor in , , and is a clique. As in the previous case, we have that , which is a contradiction because is a t-concave set.
From now on, denote by and the instances of the set and the families and of Algorithm 1 when line 1 is reached, respectively, and write and is t-concave. We will show that is a toll hull characteristic family of the input graph.
Lemma 10
Let . For every connected component of , there is such that and is t-concave.
Proof
Let be the set of minimizing the number of vertices of a connected component of and let be a connected component of with minimum number of vertices. Since is the union of mp-subgraphs of , every mp-subgraph of is also an mp-subgraph of . Hence is reducible, which means that has at least two extremal mp-subgraphs such that at least one of them is contained in . Call it by . Therefore, is contained in a member of contained in . If is not t-concave, then is not contained in one member of . By Lemma 6 , is disconnectec containing a connectec component with less vertices than , which is a contradiction. Therefore, is such that and is t-concave.
The next result is essential to show that only one vertex suffices for every set having type 1.
Lemma 11
If is such that is t-concave of type , then and for any .
Proof
Let be such that is t-concave of type 1. By Lemma 8, there are and such that . Let be a connected component of such that there is with . By Lemma 10, there is . Let be a -path of where and let be a -path of . Write as the concatenation of with the edge , and as the concatenation of with the edge . It is clear that the concatenation of with is a tolled -walk containing .
If was added to in line 1, then, is an extremal mp-subgraph of . By Theorem 2.1, , which means that . The other possibility is that was added to in line 1 of some iteration of the while loop. Let , , and be obtained in lines 1, 1, and 1, respectively, of iteration . Observe that every vertex has a non-neighbour because otherwise would be a clique, which would mean that is contained in some mp-subgraph of , which would imply that belongs to by the construction of . Observe that Lemma 7 implies because otherwise it would exist an mp-subgraph outside containing which was not used to compose . Therefore, and we can assume that because for any set , there is no edge between a vertex of and a vertex of .
For every set added to such that has type 1, we associate a natural number . It is clear that a set is added to at most once and this occurs in lines 1 or 1. If is added to in line 1, set . If is added to in line 1 of iteration and is not t-concave for every , then set . Otherwise, define and is t-concave. Lemma 9 guarantees that if is t-concave for in iteration , then the type of is 1. It is clear that is well defined. We use induction on to prove that . For the basis, consider .
Suppose that Choice 2 is possible in line 1. Let be a -path of , and let be a -path of . It is clear that the paths , , and form a tolled -walk. For every , it holds that because . Observe that and . Since , . By Lemma 6 , is a clique, which means that is contained in an mp-subgraph forming and that every vertex of has a neighbor in . By Lemma 6 , induces a connected graph. Then, there is a -walk containing all vertices of . Therefore, for every .
Now, we show that Let be the vertex sets of the connected components of . On the one hand, and are incomparable sets. Since and are connected graphs, for , contains a vertex having a neighbor in and a vertex having a neighbor in . By Lemma 6 , there is a -path of and there is a -path of . Now, for each set , the paths and can be used to find a tolled -walk such that, using Lemma 4, we can conclude that . On the other hand, . Therefore, and we can assume that and belong to , and a tolled -walk containing all vertices of also exists. Then, .
Next, we show that . We claim that for every , there are for which there is a tolled -walk containing . If is clique, then, since is contained in an mp-subgraph composing , is connected, and the follows from Lemma 4 and the fact that is not t-concave. Then, consider that is not a clique and let be a connected component of . Then, there are non-adjacent vertices both having neighbors in and sets such that and . Since we have already shown that and , for any vertices and , there is a tolled -walk containing a vertex of . Using Lemma 4 again the proof of the claim is complete.
If , then we are done. The other possibility is that exactly one of and , say , does not belong to . Therefore, has a non-neighbor in , which means that there is a tolled -walk containing , which implies that .
It remains to show that . Let . There is some such that . Then, has a neighbor in and a neighbor in . Since we know that and , it holds that .
We now consider that only Choice 3 can be done. On the one hand, for every . For every , note that and both have neighbors in . Therefore, there is a -walk in containing a vertex . The concatenation of , , and is a tolled -walk containing . By Lemma 6 , is connected. By Lemma 4, . The same reasonings of the previous case can be used here to show that and that .
On the other hand, there is such that . Note that for every such that , is complete. For instance, this is the case for and for the set chosen as . Since is not t-concave, there are vertices for which there is a tolled -walk containing some vertex . Therefore, for such that , . Furthermore, and have both non-neighbors in . Without loss of generality, there is such that . As in the previous case, . In fact, for every such that . Since , , i.e., . If , then using the same reasoning, we conclude that . Otherwise, consider . Therefore, . More generally, let such that . Not that dominates . Then, is a -walk containing a tolled -walk passing through . The same reasoning of the previous case can be used here to show that and that .
Now, consider and that the result holds for every t-concave set added to such that . Hence contains at least one member such that is t-concave of type 1, say . By the induction hypothesis, there is and . We claim that some vertex of has a non-neighbor in . Suppose the contrary. Since is not t-concave, there exist vertices such that . Since both and have at least one non-neighbor in and, by assumption, is t-concave, at least one, say , belongs to . Since every vertex of has a neighbor in , we conclude that a tolled -walk containing some vertex of can be modified to contain a vertex of , which is not possible because is t-concave. Therefore, let having a non-neighbor . Recall that is a vertex non-adjacent to . Therefore, there is a tolled -walk containing vertices and . Note that for , induces a connected graph by Lemma 6 . Hence, there is a walk containing all vertices of that can be concatenated with forming a tolled -walk containing all vertices of . Therefore, for every . It remains to show that . Let . This means that there is such that , and consequently that has a neighbor in and a neighbor in . Since and , we conclude that . Therefore, .
The above proof has the following consequence.
Corollary 3
If was obtained in line 1 of Algorithm 1 such that has type , then the family obtained in line 1 of the same iteration has at most one member such that is t-concave.
Now, we show that the toll hull number of is if has type 2.
Lemma 12
If is such that is t-concave of type , then and for .
Proof
Let such that is t-concave of type . If was added to in line 1, then the result follows from Corollary 1 because is an mp-subgraph. Now, we consider that was added to in line 1 of some iteration of the while loop. Let , and be obtained in lines 1, 1, and 1 of iteration , respectively. If , then it would exist a vertex of having a non-neighbor in , which would imply that the type of is not 2. Then, . By Lemma 9, the number of sets of such that is t-concave is at most .
If and Choice 5 was done in line 1, then, by Lemma 4, it holds for every because induces a connected graph for by Lemma 6 . If and Choice 6 was done in line 1, or and Choice 7 was done in line 1, or , then, by Lemma 4, it holds for every because induces a connected graph by Lemma 6 . If and Choice 8 was made, then for every .
Therefore, it remains to show that for Choice 5 or 8 and that for or Choice 6 or 7.
First consider . For Choice 5, since no vertex outside belongs to a tolled walk containing vertices of , there are vertices whose tolled interval contains vertices of . Since we have already shown that , we have
For Choice 6, we can assume that Choice 5 is not possible. Therefore, for some , there is such that or there are and such that . For both cases, we have that because we have already shown that . It is clear that the same does hold for .
For , consider first that Choice 7 is possible. From Lemma 11, it holds that . Now, since no vertex outside belongs to a tolled walk containing vertices of , it holds , and we have already shown that . For Choice 8, From Lemma 11, it holds that .
For , the result follows directly from Lemma 11.
It remains to show that . Let . We know that also belongs to a set . If , then observe that belongs to a tolled -walk. Otherwise, observe that belongs to a tolled -walk where . Since , .
Theorem 3.1
Algorithm 1 is correct.
Proof
For every set such that is t-concave, if has type for , then ; and if , then . Since every vertex of belongs to for some such that is t-concave and for any , by Lemma 5, it suffices to show that is a toll hull set of . We also have as a consequence that is a toll hull characteristic family of .
First, consider . Then, , which means that the type of is 2 or 3. In the former case, the result follows from Lemma 12 and, in the latter case, is a complete graph and the result follows from Corollary 1.
Now, consider and let such that is t-concave. First, we show that . If has type 3, then by line 1. If has type 2, then by Lemma 12. In the remaining case, has type 1. By Lemma 11, it suffices to show that contains a set of . But this is a consequence of Lemma 6 and the fact that .
It remains to show that every belongs to . Suppose the contrary and let such that is connected and is maximal. Write . Note that belongs to a member of or to a member such that is not t-concave. In the former case, is disconnected by Corollary 2. In the latter case, by the assumption on , is not contained in any another member. Then, Lemma 6 implies that is disconnected. In both cases, each of the connected components contains a member of , i.e., there are vertices belonging to different connected components of . It is clear that any tolled -walk contains some vertex of , which is a contradiction.
The task of deciding whether a set is t-concave appears many tymes in Algorithm 1. A direct application of Lemma 3 leads to an algorithm for deciding whether a set is t-concave with time complexity equals . However, all sets that this test is necessary in the algorithm have particular properties. In the next result, we show how to decide whether these sets are t-concave in steps.
Lemma 13
Let be a graph with vertices. If is such that is a clique and is connected, then to test whether is t-concave can be done in steps.
Proof
By definition, is not t-concave if and only if there are such that there is a tolled -walk containing some vertex of . The assumption that is a clique implies that . Since is connected, by Lemma 4, either all vertices of belong to or none. Let . Since , by Lemma 3, is in some tolled walk between non-adjacent vertices if and only if does not separate from and does not separate from .
For every , denote by the family formed by the vertex sets of the connected components of . Using an standard search algorithm, all families can be found in steps. Now, observe that belongs to if and only if there are with such that . Since this test can be done in steps, the proof is complete.
Theorem 3.2
For an input graph with vertices, Algorithm 1 runs in steps.
Proof
Lines 1 and 1 can done in using the algorithm in Leimer (1993). The construction of and in lines 1 and 1 can be done in . The number of iterations of the for loop is . Using Lemma 13, one can test whether a set is t-concave in steps. Since the time complexity of each Choice for is clearly , lines 1 to 1 can be done in steps.
Every time that line 1 is reached, we already know for each member of , whether is not t-concave. The conditions of line 1 can be checked in considering the intersection of the mp-subgraphs forming the members of . Clearly, each operation from line 1 to 1 can be done in . Since Line 1 can be done in time by Lemma 13 and lines 1 to 1 can be done in , the while loop costs , which is the overall time complexity of Algorithm 1.
4 Concluding remarks
We conclude discussing some consequences of Algorithm 1. First, we observe that the number of minimum toll hull sets can be exponential on the size of the graph. However, using the toll hull characteristic family constructed by Algorithm 1, one can enumerate all minimum toll hull sets of with polynomial time delay. For this, it suffices to change the choices used by the algorithm so that they find all possible selections for a concave set accordingly to the appropriate choice, i.e., if has type 1, let be formed by all vertices such that satisfies the appropriate choice for ; and if has type 2, let be formed by all pairs such that satisfies the appropriate choice for . Therefore, the algorithm of enumaration consists of finding all combinations considering the possible choices for each concave set of the toll hull characteristic family.
Another consequence of Algorithm 1 together with the notion of granularity is a characterization of toll extreme vertices of a graph. As discussed in Alcón et al. (2015), the property of a vertex being an extreme vertex is not well-behaviored in toll convexity as in other well-studied convexities, such as geodetic, nonophonic, and convexities, where the neighborhood of the vertex has all information to answer the question. Using the toll hull characteristic family of Algorithm 1, we have the following characterization of the toll extreme vertices of a graph.
Corollary 4
The set of toll extreme vertices of is formed by the vertices belonging to the sets of having type .
Proof
Let be formed by the vertices belonging to the sets of having type . Suppose by contradiction that there is such that is an extreme vertex. Since every non-simplicial vertex is not an extreme vertex, is a clique. Observe that is the vertex set of an mp-subgraph of and . On the one hand, . Since , . By Corollary 2, is disconnected, which means that is not an extreme vertex. On the other hand, is an mp-subgraph composing a set . Since is an extreme vertex, is t-concave. Therefore, in some iteration of the while loop, such that is not t-concave. Then, there are of vertices such that there is a tolled -walk containing some vertex of . Since is complete, . Let and vertices of such that . Denoting the subwalk of from to and the subwalk of from to , Note that the concatenation of , , and is a tolled -walk containing , which is a contradiction.
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