On the Parameterized Complexity of Computing Balanced Partitions in Graphs^{†}^{†}thanks: An extended abstract of this article appeared at the 39th International Workshop on GraphTheoretic Concepts in Computer Science (WG 2013) [50]. The extended abstract contains results regarding Bisection and Vertex Bisection, while this article additionally provides full proof details as well as parameterized complexity analyses of Balanced Partitioning. This article is published in Theory of Computing Systems, available at link.springer.com.
Abstract
A balanced partition is a clustering of a graph into a given number of equalsized parts. For instance, the Bisection problem asks to remove at most edges in order to partition the vertices into two equalsized parts. We prove that Bisection is FPT for the distance to constant cliquewidth if we are given the deletion set. This implies FPT algorithms for some wellstudied parameters such as cluster vertex deletion number and feedback vertex set. However, we show that Bisection does not admit polynomialsize kernels for these parameters.
For the Vertex Bisection problem, vertices need to be removed in order to obtain two equalsized parts. We show that this problem is FPT for the number of removed vertices if the solution cuts the graph into a constant number of connected components. The latter condition is unavoidable, since we also prove that Vertex Bisection is W[1]hard w.r.t. .
Our algorithms for finding bisections can easily be adapted to finding partitions into equalsized parts, which entails additional running time factors of . We show that a substantial speedup is unlikely since the corresponding task is W[1]hard w.r.t. , even on forests of maximum degree two. We can, however, show that it is FPT for the vertex cover number.
1 Introduction
In this article we consider partitioning problems on graphs. These are clusteringtype problems in which the clusters need to be equalsized, and the number of edges connecting the clusters needs to be minimized. At the same time the desired number of clusters is given. We begin with the setting in which only two clusters need to be found, and later generalize to more clusters. In particular we consider the Bisection, Vertex Bisection, and Balanced Partitioning problems, which are defined below. We study these problems from a parameterized complexity point of view and consider several parameters that naturally arise from the known results (see Table 1). That is, we consider a given parameter of an input instance and ask whether an algorithm with running time exists that optimally solves the problem. Here is the instance size and is a function that only depends on . If there is such an algorithm, then the problem is called fixedparameter tractable (or FPT for short) with respect to . For indepth introductions to parameterized complexity we refer to the literature [46, 26, 19]. Throughout this article we use standard terminology of graph theory [17].
Problem  Parameter  Results 
Bisection  cut size  FPT for planar graphs [9] 
FPT in general [14]  
No polysize kernel (Theorem 6)  
treewidth  FPT [49, 52]  
No polysize kernel (Theorem 6)  
unionoblivious (e.g. bandwidth)  No polysize kernel (Theorem 6)  
cliquewidth deletion number  FPT (Theorem 7)  
cliquewidth  XP, W[1]hard [27]  
Vertex Bisection  cut size  FPT if nr of cut out components is constant (Theorem 5) 
cut size &  
nr of cut out components  W[1]hard (Theorem 1)  
Balanced Partitioning  cut size &  
nr of cut out components  W[1]hard (Theorem 9)  
treewidth  NPhard for trees [23]  
cliquewidth  NPhard for cluster graphs [1]  
vertex cover  FPT (Theorem 10)  
nr of parts in the partition  W[1]hard for forests (Theorem 8) 
1.1 The Bisection problem
The first problem we consider is the NPhard [32] Bisection problem for which the vertices of a graph need to be partitioned into two parts and of size at most each, while minimizing the number of edges connecting and . The partition is called a bisection of , and the number of edges connecting vertices in with vertices in is called the cut size. Throughout this article it will be convenient to consider Bisection as a decision problem, which is defined as follows.
Bisection
 Input:
A graph and a positive integer .
 Question:
Does have a bisection with cut size at most ?
The Bisection problem is of importance both in theory and practice, and for instance has applications in divideandconquer algorithms [41], computer vision [40], and route planning [15]. As a consequence, the problem has been thoroughly studied in the past. It is known that it is NPhard in general [32] and that the minimum cut size can be approximated within a factor of [48]. Assuming the Unique Games Conjecture, no constant factor approximations exist [37]. For special graph classes such as trees [42] and solid grids [24] the optimum cut size can be computed in polynomial time. For planar graphs it is still open whether Bisection is NPhard, but it is known to be FPT with respect to the cut size [9].
It was recently shown by Cygan et al. [14] that Bisection is FPT with respect to the cut size on general graphs. We complement this result by showing that Bisection does not allow for polynomialsize problem kernels for this parameter unless coNPNP/poly. Hence, presumably there is no polynomialtime algorithm that reduces an instance of Bisection to an equivalent one that has size polynomial in the desired cut size. We prove this by giving a corresponding result for all parameters that are polynomial in the input size and that do not increase when taking the disjoint union of graphs. We call such parameters unionoblivious. This includes parameters such as treewidth, cliquewidth, bandwidth, and others.
Some of these parameters have been considered for the Bisection problem before. For instance, we already mentioned the cut size, and it was shown that the problem is FPT with respect to treewidth [49, 52]. Even though treewidth is probably the most widely used graph parameter for sparse graphs, it is not suitable for dense graphs, although they can also have simple structure. For that purpose, Courcelle and Olariu [13] introduced the parameter cliquewidth [21]. Fomin et al. [27] showed that Bisection is W[1]hard with respect to cliquewidth, that is, an FPTalgorithm is unlikely. On the positive side, they give an time algorithm if a cliquewidth expression is given. Generalizing the latter result we show that Bisection is FPT with respect to the cliquewidth vertex deletion number: the number of vertices that have to be deleted in order to obtain a graph of constant cliquewidth .^{1}^{1}1To be precise, we need the vertex deletion set to be given to obtain an FPT algorithm for this parameter. To the best of our knowledge this parameter has not been considered in the past. The cliquewidth deletion number is a generalization of several wellstudied graph parameters like vertex cover number () [12], cluster vertex deletion number and cograph vertex deletion number () [13], feedback vertex set number () [39], and treewidth vertex deletion number () [13, 28].
1.2 The Vertex Bisection problem
The next problem we consider is the Vertex Bisection problem, for which vertices instead of edges need to be removed in order to bisect the graph. More formally, let be a graph and be a subset of the vertices of . We call an separator for if there are vertex sets such that forms a partition of , and there are no edges between and in . Moreover, we call balanced if . The problem then is the following.
Vertex Bisection
 Input:
A graph and a positive integer .
 Question:
Does contain a balanced separator of size at most ?
We show that this problem is more general than Bisection in the sense that any solution to Vertex Bisection can be transformed into a solution to Bisection having (almost) the same cut size and (almost) the same number of cut out connected components, in polynomial time. In contrast to Bisection however, we prove that Vertex Bisection is W[1]hard with respect to the cut size. In fact this still holds true when combining the cut size and the number of cut out connected components as a parameter. This means that to obtain a fixedparameter algorithm it is unavoidable to impose some additional constraint.
We show that the Vertex Bisection problem is FPT with respect to the cut size, if an optimal solution cuts the graph into a given constant number of connected components. We chose this condition as a natural candidate: First, in practice optimal bisections often cut into very few connected components, typically only into two or three [2, 36, 51, 16]. Second, also for random regular graphs the sets and of the optimum bisection are connected with high probability [10]. And third, Vertex Bisection remains NPhard even if all optimum bisections cut into exactly two connected components (this follows easily by combining the NPhardness proof for Bisection of Garey et al. [32] with our techniques from Section 2.2; see also [8] for a related problem). To achieve our FPT result for Vertex Bisection, we generalize the treewidth reduction technique for separation problems that has been recently introduced by Marx et al. [45]. By adapting it to the global balancedness constraint of our problem, we address an open question by Marx et al. [45] of whether this is possible.
1.3 The Balanced Partitioning problem
Apart from Bisection and Vertex Bisection we also study the Balanced Partitioning problem. This is a natural generalization of the Bisection problem, in which the vertices of a graph need to be partitioned into equalsized parts, for some arbitrary given number (instead of only two). More formally the problem is defined as follows, where the cut size of a partition is the number of edges incident to vertices of different parts.
Balanced Partitioning
 Input:
A graph and two positive integers and .
 Question:
Is there a partition of the vertices of into sets of size at most each and with cut size at most ?
Note that there is no explicit lower bound on the part sizes. This means that they can technically speaking be unbalanced. However the definition above is the most commonly used one in the literature. Also, for numerous applications such as parallel computing [3] or VLSI circuit design [4], only an upper bound is needed.
Our algorithms for the special case Bisection can easily be extended to algorithms for Balanced Partitioning, as we will describe in Section 5. However the algorithms have additional running time factors in the order of . We observe that Balanced Partitioning is W[1]hard for the number of cut out parts even on forests of maximum degree two and hence it is unlikely that running time factors of can be avoided. Furthermore, we show that the problem remains W[1]hard on more general graphs for the larger number of cut out connected components.
Regarding structural graph parameters, many of the known hardness results for Balanced Partitioning already rule out FPT algorithms for parameters such as treewidth or cluster vertex deletion number (Balanced Partitioning is NPhard for trees [23] and graphs formed by a disjoint union of cliques [1]). On the positive side we can show that Balanced Partitioning is FPT with respect to the vertex cover number . Recently Ganian and Obdržálek [30] developed an algorithmic framework with which they were able to show that Balanced Partitioning is FPT with respect to the combined parameters and . Hence we improve on this result by removing the dependence on .
1.4 Organization of the article
We begin with presenting our results for Vertex Bisection in Section 2. These include the hardness of Vertex Bisection, the FPT algorithm in case the number of cut out components is constant, and the reduction from Bisection showing that Vertex Bisection is more general. Section 3 contains incompressibility results for Bisection. In Section 4 we give our FPT algorithm for the cliquewidth deletion number. Hardness results for Balanced Partitioning are given in Section 5, and Section 6 contains the FPT algorithm w.r.t. the vertex cover number.
2 Vertex Bisection and the Cut Size Parameter
In this section we show how to compute optimal bisections that cut into some constant number of connected components in FPTtime with respect to the cut size. As mentioned in the introduction, for Vertex Bisection one searches for a small set of vertices in order to bisect a given graph. We note below that Vertex Bisection generalizes Bisection and hence, there is also a corresponding algorithm for Bisection.
The outline of this section is as follows. First, we note that Vertex Bisection is W[1]hard with respect to and the number of cut out components (Section 2.1). Hence, an additional constraint like being constant is unavoidable to get an FPTalgorithm. We then proceed to show that Vertex Bisection indeed generalizes Bisection (Section 2.2). The FPT algorithm with respect to and constant number of cut out components for Vertex Bisection is given in Section 2.3.
2.1 Hardness of Vertex Bisection
In this section we prove that Vertex Bisection is W[1]hard with respect to the combination of the desired separator size and the number of cut out components. It follows, that it is in particular W[1]hard for the parameter separator size.
Theorem 1.
Vertex Bisection is W[1]hard with respect to the combined parameter , where is the desired separator size and is the maximum number of components after removing any set of at most vertices from the input graph.
We reduce from the W[1]hard Clique problem [19]. The reduction is an adaption of the one Marx [43] used to show W[1]hardness for the Cutting Vertices problem. The construction we use is as follows.
Construction 1.
Suppose we want to construct an instance of Vertex Bisection from an instance of Clique. Without loss of generality, assume that is even. The graph is obtained by first copying and then subdividing every edge, meaning to replace each by a new edge vertex and the edges . Next, we make into a clique in and we furthermore add to a disjoint clique with vertices, where and . Note that the overall number of vertices in is .
Let us prove that Construction 1 is a parameterized reduction.
Proof of Theorem 1.
Assume that contains a clique of size . We claim that also induces a balanced separator in . Let be the set of edge vertices in corresponding to the edges of . Consider the sets and . Clearly, form a partition of , and since is exactly the neighborhood of in , there are no edges between and . Moreover,
Thus, indeed is a balanced separator for .
For the reverse direction first consider any vertex set with . The graph consists of two cliques that, without loss of generality, contain more than vertices, in addition to degreetwo vertices attached to the clique on . Furthermore, any pair of vertices in has at most one common degreetwo neighbor. Hence, the number of connected components of is at most , which means that .
Now assume additionally that is a balanced separator for and, without loss of generality, assume that . We may furthermore assume that . Otherwise we may successively replace all vertices in with arbitrary vertices from , which is always nonempty since
Note that, without loss of generality, we may assume further that is even. Otherwise, because is even by assumption and we may simply add an arbitrary vertex from or to . Hence, since is also even and , we have . Thus, in addition to the vertices in the set needs to get at least
more vertices. Hence, needs to separate edge vertices from . This can only be achieved if , and hence induces a clique of size in . ∎
2.2 Reducing Bisection to Vertex Bisection
Before turning to our algorithm for Vertex Bisection we show that it indeed transfers also to Bisection. That is, Vertex Bisection is more general than Bisection in our setting. We say that is a component separator for if there are exactly connected components in . A component bisection is defined analogously.
Theorem 2.
There is a polynomialtime manyone reduction from Bisection to Vertex Bisection such that the desired separator size is one larger than the desired cut size. Furthermore, each component bisection for the Bisection instance yields a component balanced separator for the Vertex Bisection instance and vice versa.
The basic idea to prove Theorem 2 is to subdivide each edge and replace each vertex by a large clique in a given instance of Bisection. As deleting edge vertices corresponding to cut edges in a bisection then yields imbalanced parts, we use a gadget consisting of two very large cliques connected by a path to rebalance the parts.
Construction 2.
Let be an instance of Bisection and without loss of generality, assume that and denote . Construct a graph as follows. For each vertex , introduce a clique with vertices. Next, for every edge introduce a vertex and make adjacent to all vertices in both and . Call the set of such “edge vertices” . Finally, add two cliques and with vertices each and connect two arbitrary vertices of and by a path with inner vertices. Set the desired separator size to . This finishes the construction of a Vertex Bisection instance .
We claim that Construction 2 yields a proof for Theorem 2.
Proof of Theorem 2.
First, Construction 2 can easily be seen to be doable in polynomial time. Let us prove that it is a manyone reduction.
Let be a bisection of , that is such that and there are at most edges between and in ; call the set of these edges . Let , , and . Note that is an separator of . Furthermore . Observe also that for any there is a vertex on such that is a separator of and . Hence, choosing on appropriately yields a balanced separator for that has size . Further, if is a component bisection, then is a component balanced separator.
For the converse direction, let be a balanced separator for . Observe that, for the cliques and for each of the cliques , , removing from their vertex set yields a set which is completely contained either in or in . Furthermore, not both and are contained in or in since, otherwise, this would contradict the balancedness of . Let us assume without loss of generality, that and . We claim that the number of cliques , , that intersect is the same as the number of cliques , , that intersect . By the above observation, all vertices of a clique not contained in are either completely contained in or . Hence, as consists of , its intersection with the cliques , at most edgevertices , and at most vertices from , we have
and analogously for . Without loss of generality, we may assume . Using the above size bounds for and , we thus obtain that is at least
and, thus, (recall that is a balanced separator, and hence ). Since and we have at least one vertex in , and thus the number of edge vertices in the balanced separator is at most . We conclude that cutting the edges according to the edge vertices in yields a bisection for of cut size at most . Here, too, the bound on the number of connected components is easy to see. ∎
2.3 An FPT Algorithm for Cut Size and Constant Number of Cut Out Components
We now outline an FPT algorithm for Vertex Bisection. We say that is an separator for vertices if there are vertex sets such that is an separator and and . We say that an separator is inclusionwise minimal, or just minimal, if there is no separator . We first observe that a balanced separator consists of inclusionwise minimal separators between a collection of “terminal” vertices . The terminal vertices are chosen one from each of the connected components of the graph without the separator. Guessing the terminals, we can reduce Vertex Bisection to finding an “almost balanced” separator consisting of vertices contained in inclusionwise minimal separators of pairs of terminals. To find such an almost balanced separator, we generalize the “treewidth reduction” technique introduced by Marx et al. [45]. We obtain an algorithm that constructs a graph that preserves all inclusionwise minimal separators of size at most between some given terminals and has treewidth bounded by some function , where is the number of terminals. Moreover, the algorithm runs in time and also derives a mapping of the vertices between the input graph and the constructed graph which allows to transfer balanced separators of to balanced separators of . Using this algorithm it then only remains to show that weighted Vertex Bisection is fixedparameter tractable with respect to the treewidth. Overall, the algorithm solving Vertex Bisection guesses the terminals, reduces the treewidth and then solves the boundedtreewidth problem.
The main ingredient in our FPT algorithm for Vertex Bisection is a generalization of the treewidth reduction technique of Marx et al. [45] to graphs with vertex weights. We aim to construct a graph of bounded treewidth that preserves all inclusionwise minimal separators of a given size. To this end, we define trimmers.
Definition 1.
Let be a graph, an integer and . A tuple of a graph and a total, surjective, but not necessarily injective mapping is called a trimmer of if the following holds. (Here, we let , for , and define analogously.)

For any , the mapping is a onetoone mapping between the connected components of and .

If is an inclusionwise minimal separator for with and , then and is an inclusionwise minimal separator for .
An example for a trimmer is given in Figure 1: one can verify by inspecting the graph depicted there, that only vertices 1, 2, 11, 12, 13, 14, and 15 are contained in any inclusionwise minimal separator of size at most 3. For example, vertex 3 is not contained in any such separator because removing it from the graph leaves two vertexdisjoint paths and all pairs of such paths can only be destroyed by removing two further vertices if we delete either vertex 1 and 2, or vertex 11 and 12. However, both and are themselves separators and hence adding 3 does not yield an inclusionwise minimal separator. Thus contracting every edge in we can derive a mapping that fulfills trimmer property (ii). Basically, trimmer property (i) is obtained by observing that contracting edges keeps intact all important paths.
A more precise description of computing a trimmer, a formal proof of the properties, and an upper bound on the treewidth of the trimmer is given in Section 2.3.1: as we show in Theorem 3, if and are small, then there are trimmers of small treewidth that can be computed efficiently.
Theorem 3.
Let be a graph. For every constant and constantsize , we can compute a trimmer for in time such that the treewidth of is at most for some function depending only on and .
The final ingredient for our FPT algorithm for Vertex Bisection is an efficient algorithm for small treewidth and vertex weights.
Theorem 4.
Let be a graph with treewidth and integer vertexweights . Let be the sum of all vertex weights and let be an integer. We can find in time, for all integers , a partition of such that and is a minimumweight component separator, or reveal that no such partition exists.
We defer also this proof until later in Section 2.3.2. If we suppose for the moment that the above Theorem 3 and Theorem 4 hold, then we arrive at the main theorem of this section.
Theorem 5.
Let be a graph. Given nonnegative integers and , in time we can find a component balanced separator for of size at most if it exists. Here, is a function depending only on and .
Proof.
The algorithm proceeds as follows. For each of size exactly we compute a trimmer using Theorem 3. We create a vertex weight function for by letting . Then, for each , , we compute a minimumweight component separator for and the corresponding sets , with using Theorem 4. If among the separators there is an separator with , then we compute , and . Note that, by trimmer property (i), is a component separator for . Moreover, since is a total mapping, . We move vertices from or to in such a way that is a component balanced separator for and we output . If no suitable separator is found, we output that there is no component balanced separator of size at most for . Note that, unless is bounded by a function of (i.e. the problem is trivially FPT), moving the vertices from or to without changing the number of components of is always possible. This is because not every vertex of a connected component can separate it into multiple ones and there is always a component of size at least two.
Let be a component balanced separator of size at most for and pick vertices , one from each connected component of . Let us observe that the above algorithm finds a component balanced separator of size at most . Note that is a separator for each . Hence, contains inclusionwise minimal separators of size at most . Let , call a connected component in odd if it does not contain any , and let be the union of and all odd components. Note that odd components are contained in . Hence, is a component separator for with and . Clearly, at some point in the algorithm and the trimmer of is computed. It remains to show that separator induces a separator in that is found by the algorithm. By trimmer property (ii) we have that is contained in . Trimmer property (i) gives that is a onetoone mapping of connected components in and their counterparts in . In particular, there is a corresponding mapping for all odd connected components. Thus, is a component separator for and we have , and . Hence, an separator for with and is enumerated by the algorithm of Theorem 4. Applying the size bounds of we have and . Thus, the algorithm described above finds a component balanced separator of size at most for .
Concerning the running time, there are at most computations of the trimmer, each of which can be done in time (Theorem 3), where . Then we compute the separators for and since the treewidth of is bounded by some function (Theorem 3), this can be done in time using Theorem 4. Next, the parts can be computed in linear time from the parts and we can modify the algorithm for Theorem 4 to also output without increasing the running time bound (see Section 2.3.2). Finally, moving the vertices from the parts or to can be done in time because we move at most vertices and a vertex that does not change the number of components can be found in time by taking a leaf of a BFS tree of a component that contains at least two vertices. Hence, the overall running time is bounded by which in turn is bounded by for a suitable function . ∎
We remark that an upper bound on the treewidth of the trimmer is (see Remark 2 below), yielding a time algorithm. This bound can most certainly be improved, and it would be interesting to know what kinds of lower bounds exist. Applying Theorem 2 we can derive the following.
Corollary 1.
Let be a graph. Given nonnegative integers and , in time we can find a component bisection for of size at most if it exists. Here, is a function depending only on and .
Note that a direct application of Theorem 2 yields only a factor of in the running time. The trick is, however, that two of the terminals in instances created by the corresponding Construction 2 do not need to be guessed, while for the others we only have to guess in which vertex clique they appear. They can be assumed to be arbitrary vertices in the appropriate cliques. We omit the straightforward details. We remark that the FPT algorithm given by Cygan et al. [14] has a much better singly exponential runtime, even if is constant. However, as said above, we believe that the runtime of our algorithm can be significantly improved. Hence our observations might still yield faster FPT algorithms for constant in instances from practice. As described in the introduction, this would have implications for applications, as there often is a small constant.
2.3.1 Treewidth Reduction
In order to prove Theorem 3, we need to generalize the results of Marx et al. [45], which we do using the following definition.
Definition 2.
Let be a graph. The annotated torso , for a set , is a tuple of a graph and a total, surjective, but not necessarily injective mapping defined as follows. The graph is obtained from by contracting all edges that have empty intersection with and removing all loops and parallel edges created in the process. Hence, each connected component of has a corresponding vertex in which we call component vertex. The mapping is defined as the identity when restricted to , that is, for all we have . For all remaining vertices , maps to the component vertex of the connected component of which contains .
The torso is obtained from in as follows. First, make the neighborhood of each component vertex into a clique, creating shortcut edges. Then remove all component vertices.^{2}^{2}2This definition of torso is equivalent to the one used by Marx et al. [45].
Some graphs and their (annotated) torso graphs are depicted in Figure 2.
We begin with an observation about annotated torsos and their connected components if some set of vertices is removed.
Lemma 1.
Let be a graph, , and . For any , the mapping is a onetoone mapping between the connected components of and .
Proof.
We prove that maps connected components of to connected components of and then prove that it is indeed a onetoone mapping. Before proving the first part, we note that paths in translate to paths in for all and vice versa.

If is a path in , then is a walk in where multiple consecutive occurrences of a vertex in the second sequence are omitted. Hence there is a path in .
It is enough to show that maps each pair of adjacent vertices either to the same vertex or to adjacent vertices. This is clear if both are in . If both and are in then they are mapped to the same component vertex of , as they are in the same connected component of . Finally, if in and or vice versa, then and are adjacent by the definition of annotated torso.

If is a path in , then there is an path in for every .
Construct as follows. First, consider a component vertex with , if there is any. Note that and are not component vertices, because component vertices are not adjacent with each other. We know that is a connected component in and, since , also . Since is a component vertex adjacent to and , there are and in adjacent to and , respectively. Modify by replacing with a path in formed by the edges , and a path inside the component .
Next, if is a component vertex, choose an arbitrary path in . Such a path exists by a similar argument as above. Replace with in . Proceed analogously if is a component vertex. Since, in this way, we replaced all vertices in with paths that exist in , we have obtained an walk in . Hence also (ii) is proved.
For a graph and a set , let us call a set of vertices unbroken if there is a path in between any two vertices in . Note that a connected component of is an inclusionwise maximal unbroken set. Claims (i) and (ii) show that if is unbroken in , then is unbroken in and if is unbroken in , then is unbroken in .
Now let us prove that indeed provides a mapping between the connected components.

If a set is a connected component in then is a connected component in .
If is a connected component in then is unbroken in . Now for the sake of contradiction assume that there is a connected component in . Then is unbroken in and as is surjective. This contradicts being a connected component. Hence is a connected component of .
Finally, let us prove that the mapping is onetoone.

If a set is a connected component in then is a connected component in .
If is a connected component in then is unbroken in . Now for the sake of contradiction assume that there is a connected component in . Then is unbroken in and by the definition of as . This contradicts being a connected component. Hence is a connected component of . ∎
We now show that the treewidth of an annotated torso is at most one larger than the treewidth of the corresponding torso. For this we need to formally introduce the treewidth first. A tree decomposition of a graph is a pair , where is a rooted tree and is a mapping such that

for every , there is an with , and

for every the set induces a nonempty subtree of .
The sets are sometimes called bags. The width of a tree decomposition is and the treewidth of a graph is the minimum width of a tree decomposition for .
Lemma 2.
Let be a graph, let and let be of treewidth . Then, the graph in has treewidth at most .
Proof.
Let be a treedecomposition for . Note that, to obtain a treedecomposition for we only need to incorporate the component vertices and their incident edges. For this, successively consider each component vertex and let be its neighborhood in . Due to the shortcut edges between neighbors of , induces a clique in . Hence there is a bag , , that contains [38, Lemma 2.2.2.]. Add a new vertex to adjacent only to and define . Hence, the bags containing or , respectively, (still) induce a subtree in . Furthermore, each edge incident to is contained in . Note that we never need to introduce a copy of a bag that does not occur in the treedecomposition of because component vertices are not adjacent with each other. Thus, introducing a new bag for all component vertices in the abovedescribed way yields a treedecomposition for and increases the maximum bag size by at most one. ∎
Remark 1.
We mention in passing that the notion of annotated torso is more robust with respect to the treewidth than the notion of torso introduced by Marx et al. [45] in the following sense. Adding vertices to the set increases the treewidth of both and the graph in by at most one, as Corollary 2 (below) shows. However, removing a vertex from may increase the treewidth of arbitrarily. This is witnessed by a star where contains all vertices; removing the center of the star from yields a clique in the torso (see Figure 2). In contrast, the treewidth of annotated torsos cannot increase when removing a vertex from . This is because, either the graph in the annotated torso stays the same, or removing is equivalent to contracting an edge between and a component vertex. That is, the resulting graph is a minor of the original annotated torso graph.
As we observe next, annotated torsos can be computed in linear time.
Lemma 3.
Let be a graph and let . Then, and can be computed in time.
Proof.
To compute the annotated torso, color the connected components of with distinct colors . Next, create a copy of and modify by contracting all edges that have empty intersection with and removing all selfloops and parallel edges. In order to achieve a linear time bound for this, we proceed as follows (we assume to be represented as an adjacency list structure).
We start with vertices in having their adjacency lists as in and introduce new vertices (the component vertices) with empty lists. For each in we process its adjacency list and for each vertex on the list we distinguish the following cases:

the vertex is in . We make no changes to the adjacency lists and proceed with the next vertex in the adjacency list.

the vertex is in , in the component of color and is the last entry in the adjacency list of vertex . Then we delete from the adjacency list of vertex .

the vertex is in , in the component of color and is not the last entry in the adjacency list of vertex . Then we add to the end of adjacency list of and replace by in the adjacency list of vertex .
Note that if appears on the list of , it must be at the end, since we only process another vertex in after we finish processing . Hence, this procedure indeed computes according to the definition of . This proves the running time bound of computing the graph .
Using the colors of the vertices in we can create an array to compute efficiently. To compute we use an array indexed by the colors which at index contains a list of all vertices with color in . Filling the array can be done in linear time. ∎
To prove Theorem 3 we reuse some results by Marx et al. [45]. To state them we need to define the excess of a cut.
Definition 3.
Let be a graph and . Let be the minimum size of an separator in . The excess of an separator is .
The following three statements have been proved by Marx et al. [45]; they correspond to Lemma 2.11, Lemma 2.8, and Corollary 2.10, respectively.
Lemma 4 (Marx et al. [45]).
Let be two vertices of a graph and let be the minimum size of an separator. For some let be the union of all inclusionwise minimal separators having excess at most (that is, having size at most ). Then, there is an time algorithm that returns a set disjoint from such that , for some functions and depending only on and .
Lemma 5 (Marx et al. [45]).
Let be a graph and and let . We have .
Corollary 2 (Marx et al. [45]).
For every graph and sets , we have .
Finally we put all the above results together in order to prove Theorem 3. We have to only slightly adapt the proof of the treewidthreduction theorem by Marx et al. [45].
Proof of Theorem 3.
We first use Lemma 4 to compute a set for each pair such that contains each inclusionwise minimal separator of size at most . Let be the union of all the sets . Using the upper bound on the treewidth of Lemma 4 and combining it with the bound of Lemma 5, we obtain that has treewidth bounded by some function depending only on and . Hence, by Corollary 2 also has treewidth bounded by such a function. Using Lemma 2 we have a similar bound on the graph in . We claim that