Abstract 1 Introduction 2 Preliminaries 3 Sim-width, omim-width and Omim-width 4 Mim-width and linear mim-width 5 Open problems References

On the Hardness of Recognizing Graphs of Small Mim-Width and Its Variants

Max Dupré la Tour ORCID Department of Computer Science, McGill University, Montreal, Canada    Manuel Lafond ORCID Department of Computer Science, Université de Sherbrooke, Canada    Ndiamé Ndiaye ORCID Department of Mathematics, McGill University, Montreal, Canada
Abstract

The mim-width of a graph is a powerful structural parameter that, when bounded by a constant, allows several hard problems to be polynomial-time solvable - with a recent meta-theorem encompassing a large class of problems [SODA2023]. Since its introduction, several variants such as sim-width and omim-width were developed, along with a linear version of these parameters. It was recently shown that mim-width and all these variants are all paraNP-hard, a consequence of the NP-hardness of distinguishing between graphs of linear mim-width at most 1211 and graphs of sim-width at least 1216 [ICALP2025]. The complexity of recognizing graphs of small width, particularly those close to 1, remained open, despite their especially attractive algorithmic applications.

In this work, we show that the width recognition problems remain NP-hard even on small widths. Specifically, after introducing the novel parameter Omim-width sandwiched between omim-width and mim-width, we show that: (1) deciding whether a graph has sim-width = 1, omim-width = 1, or Omim-width = 1 is NP-hard, and the same is true for their linear variants; (2) the problems of deciding whether mim-width 2 or linear mim-width 2 are both NP-hard. Interestingly, our reductions are relatively simple and are from the Unrooted Quartet Consistency problem, which is of great interest in computational biology but is not commonly used in the theory of algorithms.

Keywords and phrases:
Mim-width, NP-hardness, Computational Biology
Category:
Track A: Algorithms, Complexity and Games
Copyright and License:
[Uncaptioned image] © Max Dupré la Tour, Manuel Lafond, and Ndiamé Ndiaye; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Mathematics of computing Graph algorithms
Related Version:
Full Version: https://arxiv.org/pdf/2512.06186
Editors:
Sayan Bhattacharya, Danupon Nanongkai, Michael Benedikt, and Gabriele Puppis

1 Introduction

Many hard problems on graphs become easier when the underlying graph has a simple structure, and over the years several “width” parameters have been developed to capture such structure. One of these is the mim-width (maximum induced matching width), introduced by Vatshelle [23]. A branch decomposition of a graph G is a ternary tree whose leaves represent the vertices of G, and each of its edges naturally defines a cut of the graph. For any such decomposition, each cut induces a bipartite graph, in which we consider the size of a maximum induced matching. The mim-width of G is then the minimum, over all branch decompositions of G, of the maximum size of such an induced matching across all cuts of the decomposition [23, 3].

It is a powerful parameter that can be constant even on graphs of high clique-width, and several algorithmic problems are polynomial-time solvable on graphs of constant mim-width [12, 13, 11], under the assumption that a branch decomposition is given. This is notably witnessed by a recent logic-based meta-theorem that solves many problems in polynomial-time under these assumptions [5].

Since its inception, several variants of mim-width were developed: the sim-width parameter is the same, except that edges of G within both sides of the cut are kept [14, 16]; the one-sided mim-width (omimwidth) keeps only the edges within either side of the cut and takes the minimum [7]; the large one-sided mim-width (Omimwidth), which we introduce in this work, instead takes the maximum; and the linear variant of all these parameters require the branch decomposition to be a caterpillar (see precise definitions below).

Recognizing graphs of mim-width at most k was quickly established to be W[1]-hard in parameter k [20], but the question of XP membership was posed several times since the creation of the parameter (in addition to the previous references, this was also raised in [8, 7, 18, 5]). The same remained open for all the aforementioned variants of mim-width, except the linear sim-width = 1 case shown NP-hard in [24]. All these questions were finally answered by Bergougnoux, Bonnet, and Duron [4], where the authors showed that it is NP-hard to distinguish between graphs of linear mim-width at most 1211 and graphs of sim-width at least 1216, implying the paraNP-hardness of mimwidth but also of all the above variants.

It is still open whether there is a constant c<1211 such that recognizing graphs of mim-width (and variants) at most c is in P. Determining whether such a c exists is quite relevant, because graphs of small width are those that matter for algorithmic applications. In this work, we reduce this knowledge gap significantly by proving the following results:

Theorem 1.

The problem of deciding whether a graph has sim-width, omim-width, or Omim-width equal to 1 is NP-complete. Moreover, assuming the ETH, it cannot be solved in time 2o(n), where n is the number of vertices of the graph. The same holds for the linear variant of these three parameters.

Theorem 2.

The problem of deciding whether a graph has mim-width or linear mim-width at most 2 is NP-complete. Moreover, assuming the ETH, it cannot be solved in time 2o(n), where n is the number of vertices of the graph.

Note that the ETH refers to the Exponential Time Hypothesis [10]. We also point out that the new parameter Omimwidth is a lower bound on mimwidth, and an upper bound on omimwidth, which in turn is an upper bound on simwidth.

Our reductions are relatively simple and arguably less involved than in [4], which require a series of reductions with complex gadgets. Another unique aspect of our reductions is that they are all from the Unrooted Quartet Consistency (UQC) problem, shown NP-hard in [22]. The input to UQC is a set of trees on four leaves, and one must decide whether there is a single tree that contains all of them.

This is not a common problem to reduce from, as UQC is often more seen as an “end-user” problem, in the sense that it is mostly popular for its bioinformatics applications. Its hardness has mainly been used to justify heuristics and approximations. The only notable exception we are aware of is the use of UQC to prove the NP-hardness of the so-called Triangle Ordinal Clustering problem [21], which was then used to show that recognizing the graph class of leaf powers and some of its generalizations is NP-hard [15]. Inspired by the latter, our approach shows that UQC may be well-suited for problems that require finding a branch decomposition, since UQC is a tree reconstruction problem. It is possible that UQC or similar variants may be useful for other open XP membership problems that involve graph-to-tree representations (for instance module-width [19, 3], not to be confused with modular-width, which is within a factor two of clique-width, or other similar problems stated in [9]).

Although our results do not imply hardness for mim-width and variants between 3 and 1210, they indicate that there is little hope of recognizing graphs of small width efficiently. The notable cases of mim-width 1 and linear mim-width 1 remain open. These two graph classes have exploitable structural properties, for instance graphs of mim-width 1 are weakly chordal [23] and graphs of linear mim-width 1 have no so-called asteroidal edge triples [9], and their membership in P is still possible. The aforementioned class of leaf powers is one of the most natural subclasses of graphs of mim-width 1, so its hardness may be seen as evidence for the mim-width 1 hardness. On the other hand, recognizing linear leaf powers, a subclass of graphs of linear mim-width 1, is in P [6].

2 Preliminaries

2.1 Notations and Definitions

All graphs considered are simple and undirected. The vertex set and edge set of a graph G are denoted by V(G) and E(G), respectively. An edge between two vertices u and v is denoted by uv (or equivalently vu). For a subset XV(G), let G[X] denote the subgraph of G induced by X, and let EG[X] denote the edge set of G[X]. For a set of edges FE(G), we write GF for the graph obtained from G by deleting all edges in F.

A matching of G is a set of edges no two of which share a common endpoint. An induced matching of G is a matching M such that every edge of G is incident with at most one edge of M. A cut of G is a bipartition (A,B) of V(G). We denote by G[A,B] the bipartite subgraph of G with edge set {uvE(G)uA,vB}.

Unless stated otherwise, all trees in this work are unrooted. A tree is ternary if each vertex has either 1 or 3 neighbors111 Note, ternary trees are sometimes called binary, but here we prefer “ternary” to emphasize that internal vertices have 3 neighbors, not 2.. A caterpillar is a tree in which the set of internal vertices induces a path, called its spine. Given a total order on a set P of n4 points, we define the ternary caterpillar realizing , with leaf set P as follows. If p1<p2<<pn1<pn, then the interval [2,n1] forms the spine of the caterpillar. Attach each leaf pi to the integer i, and additionally attach p1 to 2 and pn to n1. Note that a given ternary caterpillar with leaf set P realizes several total orders, since we may choose which end of the spine is the minimum/maximum and, at each end, which of the two incident leaves plays the “endpoint” role.

Width parameters

Given a cut (A,B): The mim-value of (A,B), denoted mimG(A,B), is the maximum size of an induced matching in G[A,B].

The sim-value of (A,B), denoted simG(A,B), is the maximum size of a matching in G[A,B] that is also an induced matching of G (hence, edges within A and within B are also considered but cannot be in the matching).

For a subset XV(G), the upper-induced matching number of X is the maximum size of a matching in G[X,V(G)X] that is an induced matching of the graph GEG[V(G)X].

The omim-value of a cut (A,B), denoted omimG(A,B), is the minimum of the upper-induced matching numbers of A and B.

The Omim-value of a cut (A,B), denoted OmimG(A,B), is the maximum of the upper-induced matching numbers of A and B.

Note that it directly follows from the definitions of the parameters that mimG(A,B)OmimG(A,B)omimG(A,B)simG(A,B).

mimG(A,B)=4
OmimG(A,B)=3
omimG(A,B)=2
simG(A,B)=1
Figure 1: A graph illustrating the differences of the width parameters on a specific cut (A,B).

A branch decomposition of a graph G is a ternary tree T whose leaves correspond exactly to the vertices of G. Given such a tree T, each edge e of T defines a cut (Ae,Be), where Ae and Be are the vertex sets corresponding to the two connected components of T{e}. The mim-width (resp. sim-width, omim-width, Omim-width) of T is the maximum, over all edges e of T, of mimG(Ae,Be) (resp. simG(Ae,Be), omimG(Ae,Be), OmimG(Ae,Be)). Finally, the mim-width (resp. sim-width, omim-width, Omim-width) of G is the minimum, over all branch decompositions T of G, of the mim-width (resp. sim-width, omim-width, Omim-width) of T.

Each of these width parameters has a linear variant, which require T to be a caterpillar. We denote such a variant by prefixing the parameter name with “linear”, e.g., linear-mim-width(G) is the minimum mim-width of all branch decompositions of G that are caterpillars.

The following easily follows from the definitions.

Lemma 3.

For any graph G, we have

mimwidth(G)Omimwidth(G)omimwidth(G)simwidth(G).

This chain of inequalities also holds by replacing each parameter by their linear variant. Moreover, linearmimwidth(G)mimwidth(G), and similarly for each variant of the width parameter.

2.2 Unrooted Quartet Consistency

Given a set of points P, a quartet (over P) consists of two unordered pairs of distinct elements of P that do not intersect. For clarity, such a quartet q={{pi,pj},{pk,p}} is instead denoted q=[pipj|pkp]. Slightly abusing notation, we may write pq when p is one of the four elements involved in q.

In a ternary tree T with leaves labeled {p1,,pn}, consider any four leaves pi,pj,pk,p. If there exists an edge e of T such that pi and pj lie on one side of T{e}, and pk and p lie on the other, we denote this relation by

pipjTpkp.

In this case, we say that T satisfies the quartet q=[pipjpkp]. If Q is a set of quartets, then T satisfies Q if it satisfies each qQ.

Because T is ternary, given four leaves pi,pj,pk,p, exactly one of the three configurations holds:

pipjTpkp,pipkTpjp,orpipTpjpk.

The NP-hard problem used in our reductions is the Unrooted Quartet Consistency (UQC) problem, introduced by Steel [22].

Input: A set of points P={p1,p2,,pn} and a set Q of quartets over P.
Question: Does there exist a ternary tree T with leaves labeled by the points in P that satisfies every quartet in Q?

The hardness of UQC is proved by a reduction from Betweenness, a classical problem that was shown to be NP-complete by Opatrný [17]. In that problem, the input is a set B of ordered triples whose elements are from a set of points U. One must decide whether there is a total order < on U such that, for each (a,b,c)B, one of a<b<c or c<b<a holds. Steel’s reduction proves a slightly stronger result than NP-hardness, which we will use.

Theorem 4 ([22]).

There is a polynomial-time reduction from Betweenness to UQC. Moreover, if an instance of Betweenness is a YES-instance, then there exists a caterpillar that satisfies the corresponding UQC instance.

By analyzing the chain of reductions used to prove Theorem 4, we can also obtain a slightly stronger result under ETH.

Theorem 5.

Assuming the ETH, the UQC problem cannot be solved in time 2o(n+m), where n is the number of points of an instance and m is the number of quartets.

Proof.

The result can be deduced by simply revisiting the chain of reductions resulting in the NP-hardness of UQC. The first reduction is from 3-Set Splitting to Betweenness. In 3-Set Splitting, we receive sets S of size 3 over a universe U and must color U with two colors so that no set of S is monochromatic. This cannot be solved in time 2o(|U|+|S|) under the ETH (a proof appears in [1], or more specifically in the full version on arXiv [2, Proposition 5.1], under the formulation of the Monotone NAE 3-SAT problem, which is equivalent to 3-Set Splitting).

Opatrný’s reduction from 3-Set Splitting to Betweenness [17, Lemma 2] produces from (S,U) an instance (B,UB) with |B|=2|S| and |UB|=|S|+|U|+1 (each set in S becomes two betweenness triples, each element of US has a corresponding elements in UB, plus an extra point is added). It follows that Betweenness cannot be solved in time 2o(|B|+|UB|).

Finally, Steel’s reduction [22] from Betweenness to UQC transforms (B,UB) to an instance (Q,P) with |Q|=6|B| (each betweenness triple becomes 6 quartets) and |P|=|UB|+2+4|B| (the point set consists of the elements of UB and two extra points, and each betweenness constraint adds four points). Since |P|+|Q| is linear in |B|+|UB|, UQC cannot be solved in time 2o(|P|+|Q|)=2o(n+m).

3 Sim-width, omim-width and Omim-width

We start with a single reduction showing that it is NP-hard to decide whether a given graph has sim-width, omim-width, or Omim-width equal to 1. The same holds for their linear variants. Given a UQC instance (Q,P), we define the following graph G:

  • Vertices: For each element pP, we introduce a vertex p in G. For each quartet q=[pipjpkp]Q, we introduce four additional vertices in G:

    {uiq,ujq,ukq,uq}.

    Let U denote the set of all such u-vertices, so that |U|=4|Q|.

  • Edges: For each quartet q=[pipjpkp]Q, add the edges

    piuiq,uiqujq,ujqpj, and pkukq,ukquq,uqp.

    In addition, connect every pair of vertices u,uU whenever they correspond to different quartets. That is, for uiq,ujrU, we add the edge uiqujr whenever qr.

    Observe that P forms an independent set, and that each uiqU has only two non-neighbors in U.

Figure 2: Subgraph of G corresponding to a quartet q=[pipjpkp].

Note that |V(G)|=|P|+4|Q|. The fact that |V(G)| is linear in |P| and |Q| will be used to obtain the ETH-based hardness part of our result. Note that we are not able to obtain the stronger lower bound under ETH of 2o(|E(G)|), because the number of edges in this reduction is quadratic.

The combination of Lemma 3, Theorem 5, and the following proposition will imply our first hardness result, Theorem 1.

Proposition 6.

If there is a caterpillar satisfying the UQC instance (Q,P), then linearOmimwidth(G)=1. Otherwise, if (Q,P)UQC, then any branch decomposition T of G satisfies simwidthG(T)=2, and in particular simwidth(G)=2.

We split the proof in two lemmas.

Lemma 7.

If (Q,P)UQC, then any branch decomposition T of G satisfies simwidthG(T)=2, and in particular simwidth(G)=2.

Proof.

We start by proving that simwidth(G)2.

This follows directly from the fact that the maximum induced matching in the full graph G has size at most 2. Suppose, for contradiction, that there exists an induced matching of size at least 3. Since the set P is an independent set in G, at least one endpoint of every edge in the matching must belong to U. By construction, each vertex in U is adjacent to all other vertices of U except two, which themselves are adjacent. Hence, among any three such vertices of U, there must exist at least one edge between them, contradicting the assumption that they form an induced matching.

We now prove that if (Q,P)UQC, then simwidth(G)2.

Let (Q,P)UQC, and let T be a branch decomposition of G. There must exist a quartet q=[pipjpkp]Q not satisfied by T, otherwise (Q,P) would be in UQC. Assume without loss of generality that pipkTpjp, and let e be an edge of T inducing the cut (Ae,Be) such that pi,pkAe and pj,pBe. At least one of the edges along the path piuiqujqpj must cross this cut, and similarly, one of the edges along the path pkukquqp must also cross the cut. Because there is no edge with one endpoint in the first path and the other in the second path, these two edges form an induced matching in G, and therefore simwidthG(T)2 for every branch decomposition T. Consequently, simwidth(G)2.

Lemma 8.

If there is a caterpillar satisfying the UQC instance (Q,P), then linearOmimwidth(G)=1.

Proof.

Let T be such a caterpillar, and let be a total order on P that T realizes. For each piP, define

Ci={pi}{uiqqQ,piq},

that is, Ci consists of all vertices in PU whose index is i. We extend to a total order on PU as follows.

For all pi,pjP with pi<pj, and for all xCi, yCj, we set x<y. Within each Ci, the relative position of pi and any uiq depends on the quartet q:

If q=[pipjpkp], then, since T satisfies q, we have either pi,pjpk,p or pk,ppi,pj (because the four vertices are distinct). In the former case (pi,pjpk,p) we set pi<uiq, in the latter case (pk,ppi,pj) we set uiq<pi. Note that this implies that we have either

pi<uiq<ukq,uq, or ukq,uq<uiq<pi.

We choose an arbitrary order within Ci among the u-vertices to the left of pi and among those to the right of pi. We then consider the ternary caterpillar T realizing . We now prove that OmimwidthG(T)=1, that is, OmimG(Ae,Be)=1 for every edge e of T.

Because T is a caterpillar, it suffices to consider the cuts induced by the edges of its spine. Let e be an edge of the spine, and let (Ae,Be) be the cut induced by e. Since T realizes the order , one side of the cut corresponds to an initial segment of . Thus, we have either for all aAe and bBe, a<b, or for all aAe and bBe, b<a.

To prove that OmimG(Ae,Be)=1, we need to prove that the upper induced matching numbers of Ae and Be are both equal to one. This means that there is no matching of size 2 in G[Ae,Be] that is an induced matching in the graph GEG[Ae] or in the graph GEG[Be]. Let a1b1,a2b2E(G) be two edges with disjoint endpoints such that a1,a2Ae and b1,b2Be. To prove that it is not an induced matching in GEG[Ae] or GEG[Be], it suffices to show that one of the two following conditions holds:

  1. 1.

    One of the diagonals a1b2 or a2b1 is an edge, or

  2. 2.

    Both a1a2 and b1b2 are edges.

Suppose for the sake of contradiction that neither condition holds. Each vertex piP is connected only to vertices of the form uiq. Consider two edges piuiq and pjujq with ij. By the construction of the order , we have either pi,uiq<pj,ujq or pj,ujq<pi,uiq. Given that the cut can only separate pi from at most one of its neighbours in G, it is therefore impossible for the cut (Ae,Be) to contain both edges piuiq and pjujq. Consequently, at most one vertex among {a1,a2,b1,b2} can belong to P.

Suppose that exactly one vertex among {a1,a2,b1,b2} belongs to P. Without loss of generality, assume that a1=pi. Then b1 must be of the form uiq. We are supposing that condition 1 is false and that a2b1 is not an edge. Since b1U, the only vertices in U that are not adjacent to uiq are those corresponding to the other side of the same quartet. That is, if q=[pipjpkp], then these vertices are ukq and uq. By the definition of the order , we have either pi<uiq<ukq,uq, or ukq,uq<uiq<pi. Since the edge e lies on the path between a1=pi and b1=uiq in T, the vertices ukq and uq cannot belong to Ae. This yields a contradiction.

Now suppose that all vertices {a1,a2,b1,b2} belong to U. Let q be the quartet associated with a1 and q the quartet associated with b1. As before, We are supposing that condition 1 is false, and that neither a1b2 nor a2b1 is an edge. The only vertices in U not adjacent to a1 correspond to the other side of the same quartet, so q must also be the quartet of b2. Similarly, q must be the quartet of a2.

If q=q, then we have {a1,a2,b1,b2}={uiq,ujq,ukq,uq} for some q=[pipjpkp]Q. The only edges among these vertices are uiqujq and ukquq, which are both separated by e. Consequently, the quartet [uiqujqukquq] is not satisfied by T, which, by the definition of , implies that q is not satisfied by T, and we have a contradiction.
On the other hand, if qq, then both a1a2 and b1b2 are edges, which contradicts the supposition that condition 2 holds.

Proposition 6 follows from Lemma 7 and Lemma 8. We are now ready to prove Theorem 1.

Theorem 1. [Restated, see original statement.]

The problem of deciding whether a graph has sim-width, omim-width, or Omim-width equal to 1 is NP-complete. Moreover, assuming the ETH, it cannot be solved in time 2o(n), where n is the number of vertices of the graph. The same holds for the linear variant of these three parameters.

Proof.

Let σ be one of the parameters simwidth,omimwidth,Omimwidth, linearsimwidth,linearomimwidth, or linearOmimwidth. We want to show that deciding whether σ(G)=1 is NP-complete. Membership in NP is easy since a branch decomposition is an easily verifiable certificate.

For NP-hardness, for technical accuracy we need to use Theorem 4 and reduce Betweenness to deciding σ(G)=1. Take an instance I of Betweenness and let (Q,P) be the UQC instance obtained from Theorem 4, and then obtain G as constructed above from that UQC instance. If I is a YES-instance of Betweenness, then (Q,P) UQC and by Theorem 4 there is a ternary caterpillar that satisfies Q. Then, Proposition 6 implies that linearOmimwidth(G)=1. This implies that σ(G)=1, since linearOmimwidth(G) is an upper bound on all the possible σ parameters listed above (by Lemma 3).

Conversely, if I is a NO-instance of Betweenness, then no tree satisfies the UQC instance (Q,P). By Proposition 6, simwidth(G)=2, which implies σ(G)2 since simwidth(G) is a lower bound on all the possible σ parameters listed above (by Lemma 3). Therefore, I is a YES-instance of Betweenness if and only if σ(G)=1.

Moreover |V(G)|=|P|+4|Q|, therefore an algorithm recognising graphs with σ parameter equal to one in time 2o(|V(G)|) could be used to solve UQC in time 2o(|P|+|Q|), contradicting Theorem 5 assuming ETH.

Remarks on the graph class of hard instances

Let us observe that, denoting by Ck the chordless cycle on k vertices, our construction produces a graph G that contains no induced Ck nor its complement, for any k5. Indeed, G has no such induced Ck because the pi vertices are simplicial and thus are not in a Ck, and the U vertices induce the complement of a set of disjoint C4s. Likewise, U induce no Ck in G¯. So a Ck would contain some piP, which in the cycle would be in an induced path of the form piujquiq of G¯, at which point there is no way to prolong the induced path and form a Ck. It follows that the hard instances are weakly chordal, a subclass of perfect graphs. Although all graphs of mimwidth 1 are weakly chordal [23], this is not the case for graphs of (linear) sim-width 1. We note that for chordal graphs, i.e., graphs that are Ck-free for every k4, whether linearsimwidth(G)=1 can be decided in polynomial time [24]. The set of forbidden induced cycles from our construction is therefore, in some sense, tight for the case linearsimwidth equal to 1.

Using a similar reduction for mim-width 1 (or linear mim-width 1)?

It is interesting to ponder the possibility of using a similar reduction for mimwidth=1. It appears difficult to do so and here we provide the intuitive reasons for this. The high-level idea in our reduction is to construct a graph G that includes vertices representing the elements of the UQC instance and, for each quartet q, a gadget Hq. A reduction for mimwidth=1 would have the following intended properties.

  1. 1.

    In any branch decomposition of G, if the four vertices of a quartet q do not satisfy q, then some edge of the decomposition induces a cut within Hq containing an induced matching of size 2. Thus, Hq is meant to “force” a particular shape for the subtree containing these four vertices in any branch decomposition of mim-width 1.

  2. 2.

    If the UQC instance is satisfiable, then there exists a branch decomposition of G whose mim-width is exactly 1.

The difficulty arises when we try to satisfy these conditions simultaneously. Consider two different quartets q and q with corresponding gadgets Hq and Hq. In the branch decomposition guaranteed by property 2, the subtrees corresponding to these two gadgets may overlap (it seems hard to avoid this). As a consequence, an edge of Hq used in the induced matching when q is unsatisfied (property 1) may lie in the same cut as an edge of Hq used when q is unsatisfied.

To ensure mim-width 1, we must prevent these two edges from forming an induced matching. This forces us to add blocking edges between certain endpoints of edges coming from Hq and Hq in order to destroy the unwanted induced matchings. In our reduction, the blocking edges correspond to the edges between the u-vertices corresponding to different quartets.

However, these blocking edges create a new problem: they may themselves form an induced matching of size 2 across some cut, contradicting property 2 and preventing the existence of a mim-width 1 decomposition even when the UQC instance is satisfiable. In our reduction, this corresponds to the last case in the proof of Lemma 8: the matching of the type uxquyq, uwquzq for different q,q, which is only destroyed with edges not in the cut.

In short, enforcing quartet constraints independently via the gadgets Hq inevitably causes interactions (either among the gadgets or among the blocking edges) that create new induced matchings. We found no way around this, which is why our approach does not seem extendable to mim-width 1 (or linear mim-width 1).

4 Mim-width and linear mim-width

In this section, we describe two closely related reductions. The first is used to prove the hardness of recognizing graphs with mimwidth 2, and the second establishes the hardness of recognizing graphs with linearmimwidth 2.

We modify the previous reduction by adding four new vertices per quartet. Additionally, one extra vertex is introduced in the non-linear version.

Given a UQC instance (Q,P), we define the following graph G:

  • Vertices: For each element pP, we introduce a vertex p. For each quartet q=[pipjpkp]Q, we introduce eight additional vertices:

    {uiq,ujq,ukq,uq,γiq,γjq,γkq,γq}

    Let U (resp. Γ) denote the set of all such u-vertices (resp. γ-vertices).

  • Edges: For each quartet q=[pipjpkp]Q, add the edges

    {pi,uiq},{uiq,ujq},{ujq,pj},{pk,ukq},{ukq,uq},{uq,p},

    together with

    {pi,γiq},{pj,γjq},{pk,γkq},{p,γq}.

    Moreover, add all possible edges so that Γ forms a clique, add an edge between every pair of vertices u,uU whenever they correspond to different quartets (i.e., whenever their superscript differs), and between every pair of vertices uU and γΓ, whenever they correspond to different quartets.

The graph H is obtained from G by adding an extra vertex ω, sharing an edge with every vertex in U.

Figure 3: The gadget corresponding to a single quartet q=[pipjpkp], with the extra vertex ω of H represented.

Note, the constructed graphs G and H have mimwidth at least 2, since Vatshelle argued that graphs of mimwidth 1 [23, Corollary 3.7.4] have no induced cycle of length 5 or more, whereas these graphs have such cycles of the form piuiqujqpjγjqγiqpi.

Proposition 9.

If there is a caterpillar satisfying the UQC instance (Q,P), then linearmimwidth(G)=2, and mimwidth(H)=2. Otherwise, if (Q,P)UQC then linearmimwidth(G)3, and mimwidth(H)3.

The proof has the same structure as in the previous section, but require a more involved case analysis. We split it into several lemmas.

Lemma 10.

If (Q,P)UQC then linearmimwidth(G)3.

Lemma 11.

If (Q,P)UQC then mimwidth(H)3.

The first step in the proofs of these two lemmas is similar. We begin by showing a result on any branch decomposition of G. Any branch decomposition of H can be transformed into one of G by deleting the ω-leaf. The result will therefore also apply to branch decompositions of H.

Let (Q,P)UQC, and let T be any branch decomposition of G. Assume by contradiction that mimG(T)2. There must exist a quartet q=[pipjpkp]Q that is not satisfied by T; otherwise, (Q,P) would belong to UQC.

Without loss of generality, assume that pipkTpjp is true. Let π be the set of all edges e of T inducing a cut (Ae,Be) such that pi,pkAe and pj,pBe. Note that π forms a path. Let A (resp. B) denote eπAe (resp. eπBe); that is, A (resp. B) consists of all vertices on the side of pi,pk (resp. pj,p) of π.

Claim 12.

Either γiq,γjq,γkq,γq are all contained in A, or they are all contained in B.

Proof.

Suppose, for the sake of contradiction, that there exists an edge e of π such that one of the γ-vertices lies in Ae and another lies in Be. We will show that we can always find an induced matching of size 3 in G[Ae,Be]. We proceed by a case analysis.

First, consider the case where both γiq and γkq lie in Ae. Choose x{j,} such that γxq is a γ-vertex in Be. Along the path piuiqujqpj of G, at least one edge crosses the cut (Ae,Be); likewise, at least one edge of the path pkukquqp also crosses the cut. These two crossing edges, together with the edge γiqγxq, form an induced matching in G[Ae,Be]. Indeed, the only other edges that could appear among these six vertices in G are piγiq, which lies entirely within Ae, and pxγxq, which lies entirely within Be.

Therefore, we may assume that at least one of γiq or γkq is in Be. Without loss of generality, we may assume that γiqBe. By applying the symmetric argument on the pair γjq,γq, we may assume that one of the vertices γjq or γq lies in Ae.

Suppose now that ujqAe. Then one of the edges on the path pkukquqp, together with the edges piγiq and ujqpj, forms an induced matching in G[Ae,Be]. Hence we may assume ujqBe.

Next, suppose that uiqAe. Then one of the edges on the path pkukquqp, together with the edges piγiq and uiqujq, forms an induced matching in G[Ae,Be]. Hence we may assume uiqBe.

If we had γjqAe, then one of the edges on the path pkukquqp, together with the edges piuiq and γjqpj, forms an induced matching in G[Ae,Be]. Therefore γjqBe.

It follows that γqAe, and by symmetry again, we have γkq,ukq,uqAe as well. In this situation, the three edges uqp, piuiq, and γkqγjq form an induced matching in G[Ae,Be].

This establishes that either γiq,γjq,γkq,γq are all contained in A, or they are all contained in B.

We will assume without loss of generality that all the gammas are in B. We are now ready to finish the proof of Lemma 10.

Proof of Lemma 10.

We now assume for contradiction that T is a caterpillar satisfying linearmimwidth(T)2, implying in particular that mimwidth(T)2. Note that Claim 12 is applicable to T since it holds for any branch decomposition. Now, one of pj or p is adjacent to an endpoint of π in T (recall that π is the set of edges separating A and B). Without loss of generality, suppose it is pj, and let z be the neighbor of pj on the spine of the caterpillar. Let e be the edge incident to z that is not in π, other than zpj (so, e is the next edge on the spine beyond π, going towards p). This induces a cut (Ae,Be) with p and all the γ-vertices in Be, and pi,pj,pkAe. Consequently, the edges piγiq, pjγjq, and pkγkq form an induced matching in G[Ae,Be]. This implies that linearmimwidth(T)3, which contradicts our initial assumption that linearmimwidth(T)2.

The proof of Lemma 11 is slightly more complicated, because T might not be a caterpillar. Therefore, we need a case analysis depending on which vertices of B lie in the branch containing pj and which lie in the branch containing p. The additional vertex ω is required to force an induced matching of size 3 in all configurations.

Proof of Lemma 11.

We assume for contradiction that T is a branch decomposition of H satisfying mimwidth(T)2. Let v be the endpoint of π that lies on the path between pj and p in T (thus, one incident “side” of v contains pj, one contains p, and the remaining side contains both pi and pk, see figure). Let emid be the edge from v toward pi and pk, etop the edge toward pj, and ebot the edge toward p. We denote by (Amid,Bmid), (Abot,Bbot), and (Atop,Btop) the cuts induced by emid, ebot, etop, respectively. We have Bmid=B, and we partition B as B=BbotBtop, where Bbot (resp. Btop) consists of the vertices of B in the branch toward p (resp. toward pj).

The remainder of the proof accumulates a series of facts under our contradiction assumptions, ultimately showing that every possible placement of the u-vertices, the γ-vertices, and ω leads to a 3-induced matching across one of the edges emid, ebot, or etop. Recall that we defined A=eπAe and B=eπBe.

Fact 1.

The γ-vertices γiq,γjq,γkq,γq of the quartet q all lie in A or all lie in B.

Proof.

Claim 12 still holds, because T can be turned into a branch decomposition of G by deleting the ω-leaf, which does not increase the mim-width of the decomposition.

We will now assume without loss of generality that all γ-vertices of the quartet q lie in B

Fact 2.

All u-vertices uiq,ujq,ukq,uq of the quartet q lie in B.

Proof.

Suppose not, and assume without loss of generality that uiqB or ujqB (the cases ukqB or uqB are symmetric). Then one of the edges uiqujq or ujqpj crosses the cut (Amid,Bmid) (with uiqAmid in the first case). One of the edges of the path pkukquqp must also cross the cut. The vertex γiq is in B=Bmid by Fact 1. These two edges, together with the edge piγiq, form an induced matching of size 3.

Fact 3.

The vertex ω is in B.

Proof.

Assume for contradiction that ωB. Using Fact 1 and Fact 2, all the u and γ vertices are in B=Bmid. In the cut (Amid,Bmid), the edges ωujq, piγiq, and pkγkq form an induced matching of size 3.

Fact 4.

Each side Btop and Bbot contains at least one u-vertex.

Proof.

Note that by Fact 2, each u-vertex belongs to either Btop or Bbot. If all the four u-vertices lie on the same side, say Btop, then across the cut (Atop,Btop) the edges piuiq, pkukq, and puq form an induced matching of size 3. Likewise, not all four u-vertices can lie in Bbot.

Fact 5.

At least one of γkq,γq lies in Bbot. Symmetrically, at least one of γiq,γjq lies in Btop.

Proof.

Using Fact 1, each γ-vertex is either in Bbot or Btop. Assume γkq and γq are both in Btop. Then one of the edges of the path piuiqujqpj must cross the cut (Atop,Btop). Together with the edges pkγkq and pγq, this yields an induced matching of size 3, a contradiction.

Using Fact 3, the vertex ω is either in Bbot or in Btop. In the rest of the proof, we will assume without loss of generality that ωBbot (to see why this is without loss of generality, notice that if this is not the case, then we may swap the subscripts of pi and pk, and the subscripts of pj and p without affecting the previous arguments since they all apply symmetrically to Bbot and Btop).

Fact 6.

The vertices uiq and ujq are both in Btop.

Proof.

Consider uxq with x{i,j}. Using Fact 2, uxq is either in Bbot or in Btop. Assume by contradiction that it is in Bbot. We consider two subcases depending of the position of γq. Using Fact 1, γq is either in Bbot or in Btop.

If γq is in Btop, across the cut (Abot,Bbot), the following 3 edges form an induced matching:

  • pxuxq.

  • γqp.

  • One of the edge of the path pkukqω.

Otherwise γq is in Bbot. Then across the cut (Abot,Bbot), the following 3 edges form an induced matching:

  • pxuxq.

  • We take an edge joining a γ-vertex in Btop to γq in Bbot (see Fact 5).

  • One of the edge of the path pkukqω.

Fact 7.

At least one of γjq,γkq lies in Btop.

Proof.

Using Fact 1, each γ-vertex is either in Bbot or Btop. Assume for contradiction that γjq and γkq are both in Bbot. Across the cut (Abot,Bbot), the following 3 edges form an induced matching: pjγjq, pkγkq, and uiqω (we know that uiqBtop using Fact 6).

Fact 8.

The vertex ukq is in Bbot.

Proof.

Using Fact 2, ukq is either in Bbot or in Btop. Assume by contradiction that it is in Btop. Across the cut (Atop,Btop), the following 3 edges form an induced matching:

  • piuiq. We have uiqBtop by Fact 6.

  • uqukq. We are assuming that ukqBtop. Moreover, we know that uiq,ujqBtop (Fact 6), and that the u-vertices are not all in Btop (Fact 4); therefore uq is in Bbot.

  • An edge joining a γ-vertex in Bbot to either γjq or γkq in Btop. Such an edge exists using Fact 7.

Fact 9.

The vertex γkq is the only gamma vertex in Bbot.

Proof.

Assume by contradiction that there is a γ-vertex other than γkq in Bbot. Across the cut (Abot,Bbot), the following 3 edges form an induce matching:

  • pkukq. We know that ukq is in Bbot by Fact 8.

  • uiqω. We know that uiq is in Btop by Fact 6.

  • An edge joining either γiq or γjq in Btop (Fact 5) to a γ-vertex in Bbot that is not γkq.

We can finally conclude the proof. By combining Fact 1 and Fact 9, we know that γiq and γq are both in Btop. Across the cut (Atop,Btop), the following 3 edges form an induced matching: piγiq, pγq, and ωujq (since ujq is in Btop by Fact 6). This completes the proof: in all cases we obtain an induced matching of size 3.

Lemma 13.

If there is a caterpillar satisfying the UQC instance (Q,P), then linearmimwidth(G)=2.

Proof.

As we already observed, mimwidth(G)2 because it contains induced cycles of length 6, implying linearmimwidth(G)2. We focus on the upper bound.

Let T be a caterpillar satisfying (Q,P), and let be a total order on P that T realizes. For each piP, define

Ci={pi}{uiqqQ,piq}{γiqqQ,piq},

that is, Ci consists of all vertices in PUΓ whose index is i. We extend to a total order on PUΓ as follows: for all pi,pjP with pi<pj, and for all xCi, yCj, we set x<y; within each Ci, the order is arbitrary. Let T be the ternary caterpillar realizing . We prove that mimwidthG(T)2, i.e., mimG(Ae,Be)2 for every edge e of T.

Because T is a caterpillar, it suffices to consider the cuts induced by the edges of its spine. Let e be an edge of the spine, and let (Ae,Be) be the cut induced by e. Since T realizes , one side of the cut is an initial segment of . Thus, either for all aAe and bBe we have a<b, or for all aAe and bBe we have b<a.

Let a1b1,a2b2,a3b3E(G) be three edges with pairwise disjoint endpoints such that a1,a2,a3Ae and b1,b2,b3Be. Suppose, for contradiction, that these three edges form an induced matching in G[Ae,Be].

First, note that at most one of the six vertices lies in P. Indeed, a vertex piP is adjacent only to vertices of Ci. If pi were among the six vertices, then e would separate pi from another element of Ci; by the construction of , this can happen for at most one index i.

We say that a vertex v of G is of type q for a quartet q if v is one of the eight vertices in UΓ corresponding to q (i.e., the type is the superscript q). Suppose a1b1 is an edge with a1 of type q and b1 of type qq. In our construction, the only vertices not adjacent to a1 are in P or of type q; hence b2 and b3 must each lie in P or be of type q. Symmetrically, the only vertices not adjacent to b1 are those in P or of type q, so a2 and a3 must each lie in P or be of type q. If, without loss of generality, a3 is the only possible vertex possibly in P, then a2 is of type q and b3 is of type q, which forces a2b3E(G), contradicting that the three edges form an induced matching.

Therefore, at most one of the six vertices in the induced matching is in P and the remaining vertices in UΓ all have the same type q=[pipjpkp]. The three matching edges must then be of the following kinds: γγ, uu, pu, or pγ.

We cannot have two γγ edges, since Γ is a clique and there would be an extra edge between the γ-endpoints, violating the induced property.

Assume there are two uu edges. The only possibilities are uiqujq and ukquq. Without loss of generality, let uiq,ukqAe and ujq,uqBe. By the definition of , this implies uiq,ukq<ujq,uq, hence pi,pk<pj,p in , which contradicts that T satisfies the quartet q.

Since at most one of the six vertices lies in P, it follows that exactly one matching edge is of type uu (say a1b1), exactly one is of type γγ (say a2b2), and the remaining one (say a3b3) is either pu or pγ.

Without loss of generality, let a3=piP. If b3=γiq, then b3 is adjacent to a2 (both are γ-vertices), so the matching is not induced. Otherwise, if b3=uiq, then a1b1 must be ukquq. Since e separates pi from uiq, the cut lies inside Ci; together with ukquq crossing the cut, this forces pi to lie between pk and p in the order , contradicting that T satisfies the quartet q. This contradiction shows that no induced matching of size 3 can appear in G[Ae,Be], and hence mimG(Ae,Be)2 for every spine edge e of T. Therefore, mimwidthG(T)2 and linearmimwidth(G)2.

Lemma 14.

If there is a caterpillar satisfying the UQC instance (Q,P), then mimwidth(H)=2.

Figure 4: Expansion of pi in T into the subtree corresponding to Ci in T.
Proof.

Again, mimwidth(H)2 follows from the existence of induced cycles of length 6 and we show that mimwidth(H)2.

Let T be a caterpillar satisfying (Q,P). For each piP, define

Ci={pi}{uiqqQ,piq}{γiqqQ,piq},

that is, Ci consists of all vertices in PUΓ whose index is i.

We define a new tree T from T by adding the leaf ω anywhere on the caterpillar. For each i, replace the leaf pi by a rooted subtree whose leaves are exactly the vertices of Ci. The leaf pi is attached directly to the root of this subtree, and all the other leaves lie on the other branch (see Figure 4).

We prove that mimwidthH(T)2, i.e., mimH(Ae,Be)2 for every edge e of T. Fix any edge e of T.

Let a1b1,a2b2,a3b3E(H) be three edges with pairwise disjoint endpoints such that a1,a2,a3Ae and b1,b2,b3Be. Suppose, for contradiction, that these three edges form an induced matching in H[Ae,Be].

First note that none of the six endpoints lies in P. Indeed, each piP is adjacent exactly to the vertices of Ci, and by the placement of pi in T, any cut (Ae,Be) with piAe that separates pi from some vertex of Ci must be of one of two forms. Either Ae={pi}, then there is no matching of size 3; or BeCi because e is in the subtree containing Ci{pi}, then, since pi is adjacent to every vertex of Ci, the matching would not be induced.

As in the previous proof, we say that a vertex v of H is of type q for a quartet q if v is one of the eight vertices in UΓ corresponding to q. Suppose a1b1 is an edge with a1 of type q and b1 of type qq. In our construction of H, the only vertices nonadjacent to a1 are those in P, those of type q, and possibly ω. Since no endpoint is in P, it follows that b2 and b3 must be of type q or be ω; symmetrically, a2 and a3 must be of type q or be ω. If, without loss of generality, a3 is the only vertex that might be ω, then a2 is of type q and b3 is of type q, which forces a2b3E(H), contradicting that the three edges form an induced matching.

Hence all of the endpoints of the matching that are in UΓ belong to a common type (say q). Moreover, at most one of these endpoints is ω. Thus the three matching edges must be among the following kinds: γγ, uu, or ωu.

We cannot have two γγ edges, since Γ is a clique, which would create an extra edge between γ-endpoints and violate the induced property.

Assume there are two uu edges. The only possibilities are uiqujq and ukquq. Without loss of generality, let uiq,ukqAe and ujq,uqBe. This implies that in T, the uiqujq path intersects the ukquq path (in particular, they intersect at the ends of e). By our construction of T from T, this in turn implies that in T, the pipj path intersects the pkp path, and thus T does not satisfy q. Since our transformation from T to T does not alter its quartets but only adds new ones, this contradicts that T satisfies q.

Moreover, we cannot have simultaneously a uu edge and an ωu edge, because ω is adjacent to all u-vertices, creating a forbidden cross-edge and destroying the induced property. Since there is at most one ωu edge, any third edge would have to be either uu or γγ, both of which have been ruled out. Therefore no induced matching of size 3 can exist, a contradiction.

This proves that mimH(Ae,Be)2 for every edge e of T, and hence mimwidthH(T) 2.

Theorem 2. [Restated, see original statement.]

The problem of deciding whether a graph has mim-width or linear mim-width at most 2 is NP-complete. Moreover, assuming the ETH, it cannot be solved in time 2o(n), where n is the number of vertices of the graph.

Proof.

We proceed as in Theorem 1. Take an instance I of Betweenness and let (Q,P) be the UQC instance obtained from Theorem 4. Then let G and H be obtained from (Q,P) as described above. If I is a YES-instance, then (Q,P)UQC and by Theorem 4 some ternary caterpillar satisfies Q. Then by Proposition 9 we have linearmimwidth(G)=2 and mimwidth(H)=2. If I is a NO-instance, then (Q,P)UQC and by Proposition 9 we have linearmimwidth(G)3 and mimwidth(H)3. Thus I is a YES-instance of Betweenness if and only if linearmimwidth(G)2, and if and only if mimwidth(H)2, which proves the NP-hardness of our two problems (and membership in NP is easy).

As for the ETH lower bound, we observe that |V(G)|=|P|+8|Q| and |V(H)|=|P|+8|Q|+1. Since |V(G)| and |V(H)| are linear in |P|+|Q|, Theorem 5 implies that no time 2o(n) algorithm can recognize mimwidth 2 and linearmimwidth 2 graphs under the ETH.

5 Open problems

Many questions remain open. The most important one is whether graphs of mim-width 1 and graphs of linear mim-width 1 can be recognized in polynomial time.

Our result also does not imply that deciding whether (linear) mimwidth(G)c is NP-hard for every fixed constant c>2. One possible direction is to address the following question: Is there a graph operation that transforms any graph G into a graph G satisfying mimwidth(G)=mimwidth(G)+1? Alternatively, is there a graph operation that multiplies the mim-width of any graph by a constant factor? The same question remains open for other width parameters as well.

Another question is whether UQC can be used to prove hardness of other graph-to-tree representations?

Finally, can one obtain inapproximability results, for instance via a reduction that yields an arbitrarily large approximation gap?

References

  • [1] Dhanyamol Antony, Yixin Cao, Sagartanu Pal, and R. B. Sandeep. Switching Classes: Characterization and Computation. In Rastislav Královič and Antonín Kučera, editors, 49th International Symposium on Mathematical Foundations of Computer Science (MFCS 2024), volume 306 of Leibniz International Proceedings in Informatics (LIPIcs), pages 11:1–11:15, Dagstuhl, Germany, 2024. Schloss Dagstuhl – Leibniz-Zentrum für Informatik. doi:10.4230/LIPIcs.MFCS.2024.11.
  • [2] Dhanyamol Antony, Yixin Cao, Sagartanu Pal, and RB Sandeep. Switching classes: Characterization and computation. arXiv preprint, 2024. arXiv:2403.04263.
  • [3] Rémy Belmonte and Martin Vatshelle. Graph classes with structured neighborhoods and algorithmic applications. Theoretical Computer Science, 511:54–65, 2013. doi:10.1016/J.TCS.2013.01.011.
  • [4] Benjamin Bergougnoux, Édouard Bonnet, and Julien Duron. Mim-Width Is paraNP-Complete. In Keren Censor-Hillel, Fabrizio Grandoni, Joël Ouaknine, and Gabriele Puppis, editors, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025), volume 334 of Leibniz International Proceedings in Informatics (LIPIcs), pages 25:1–25:17, Dagstuhl, Germany, 2025. Schloss Dagstuhl – Leibniz-Zentrum für Informatik. doi:10.4230/LIPIcs.ICALP.2025.25.
  • [5] Benjamin Bergougnoux, Jan Dreier, and Lars Jaffke. A logic-based algorithmic meta-theorem for mim-width. In Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 3282–3304. SIAM, 2023. doi:10.1137/1.9781611977554.CH125.
  • [6] Benjamin Bergougnoux, Svein Høgemo, Jan Arne Telle, and Martin Vatshelle. Recognition of linear and star variants of leaf powers is in p. In Michael A. Bekos and Michael Kaufmann, editors, Graph-Theoretic Concepts in Computer Science, pages 70–83, Cham, 2022. Springer International Publishing. doi:10.1007/978-3-031-15914-5_6.
  • [7] Benjamin Bergougnoux, Tuukka Korhonen, and Igor Razgon. New width parameters for independent set: One-sided-mim-width and neighbor-depth. In International Workshop on Graph-Theoretic Concepts in Computer Science, pages 72–85. Springer, 2023. doi:10.1007/978-3-031-43380-1_6.
  • [8] Benjamin Bergougnoux, Charis Papadopoulos, and Jan Arne Telle. Node multiway cut and subset feedback vertex set on graphs of bounded mim-width. Algorithmica, 84(5):1385–1417, 2022. doi:10.1007/S00453-022-00936-W.
  • [9] Svein Høgemo. Mapping graphs to trees: Partition trees, leaf powers and decompositions. PhD thesis. The University of Bergen, 2025.
  • [10] Russell Impagliazzo and Ramamohan Paturi. On the complexity of k-sat. Journal of Computer and System Sciences, 62(2):367–375, 2001. doi:10.1006/JCSS.2000.1727.
  • [11] Lars Jaffke, O-joung Kwon, Torstein JF Strømme, and Jan Arne Telle. Mim-width iii. graph powers and generalized distance domination problems. Theoretical Computer Science, 796:216–236, 2019. doi:10.1016/J.TCS.2019.09.012.
  • [12] Lars Jaffke, O-joung Kwon, and Jan Arne Telle. Mim-width i. induced path problems. Discrete Applied Mathematics, 278:153–168, 2020. doi:10.1016/J.DAM.2019.06.026.
  • [13] Lars Jaffke, O-joung Kwon, and Jan Arne Telle. Mim-width ii. the feedback vertex set problem. Algorithmica, 82(1):118–145, 2020. doi:10.1007/S00453-019-00607-3.
  • [14] Dong Yeap Kang, O-joung Kwon, Torstein JF Strømme, and Jan Arne Telle. A width parameter useful for chordal and co-comparability graphs. Theoretical Computer Science, 704:1–17, 2017. doi:10.1016/J.TCS.2017.09.006.
  • [15] Max Dupré la Tour, Manuel Lafond, and Ndiamé Ndiaye. Recognizing leaf powers and pairwise compatibility graphs is np-complete, 2025. doi:10.48550/arXiv.2510.19763.
  • [16] Andrea Munaro and Shizhou Yang. On algorithmic applications of sim-width and mim-width of (h1, h2)-free graphs. Theoretical Computer Science, 955:113825, 2023. doi:10.1016/J.TCS.2023.113825.
  • [17] J. Opatrny. Total ordering problem. SIAM Journal on Computing, 8(1):111–114, 1979. doi:10.1137/0208008.
  • [18] Yota Otachi, Akira Suzuki, and Yuma Tamura. Finding Induced Subgraphs from Graphs with Small Mim-Width. In Hans L. Bodlaender, editor, 19th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2024), volume 294 of Leibniz International Proceedings in Informatics (LIPIcs), pages 38:1–38:16, Dagstuhl, Germany, 2024. Schloss Dagstuhl – Leibniz-Zentrum für Informatik. doi:10.4230/LIPIcs.SWAT.2024.38.
  • [19] Michaël Rao. Clique-width of graphs defined by one-vertex extensions. Discrete Mathematics, 308(24):6157–6165, 2008. doi:10.1016/J.DISC.2007.11.039.
  • [20] Sigve Hortemo Sæther and Martin Vatshelle. Hardness of computing width parameters based on branch decompositions over the vertex set. Theoretical Computer Science, 615:120–125, 2016. doi:10.1016/J.TCS.2015.11.039.
  • [21] Rahul Shah and Martin Farach-Colton. On the complexity of ordinal clustering. Journal of Classification, 23(1):79–102, 2006. doi:10.1007/S00357-006-0005-3.
  • [22] Michael Steel. The complexity of reconstructing trees from qualitative characters and subtrees. Journal of Classification, 9(1):91–116, January 1992. doi:10.1007/BF02618470.
  • [23] Martin Vatshelle. New width parameters of graphs. PhD Thesis. The University of Bergen, 2012.
  • [24] Emile Ziedan, Deepak Rajendraprasad, Rogers Mathew, Martin Charles Golumbic, and Jérémie Dusart. The induced separation dimension of a graph. Algorithmica, 80(10):2834–2848, October 2018. doi:10.1007/s00453-017-0353-x.