Abstract 1 Introduction 2 Main General Theorem 3 Improved Kernel for Planar Graphs References

A Linear Kernel for Independent Set Reconfiguration in Planar Graphs

Nicolas Bousquet ORCID Université Claude Bernard Lyon 1, France
CNRS – Université de Montréal CRM – CNRS, Canada
Daniel W. Cranston ORCID Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
Abstract

Fix a positive integer r, and a graph G that is K3,r-minor-free. Let Is and It be two independent sets in G, each of size k. We begin with a “token” on each vertex of Is and seek to move all tokens to It, by repeated “token jumping”, removing a single token from one vertex and placing it on another vertex. We require that each intermediate arrangement of tokens again specifies an independent set of size k. Given G, Is, and It, we ask whether there exists a sequence of token jumps that transforms Is into It. When k is part of the input, this problem is known to be PSPACE-complete. But it was shown by Ito, Kamiński, and Ono [20] to be fixed-parameter tractable. That is, the problem can be solved in time f(k)P(n), for some function f and polynomial P, where n denotes the order of G.

Here we strengthen the upper bound on the running time in terms of k by showing that the problem has a kernel of size linear in k. More precisely, we transform an arbitrary input problem on a K3,r-minor-free graph (for some fixed positive integer r) into an equivalent problem on a (K3,r-minor-free) graph with order O(k). This answers positively a question of Bousquet, Mouawad, Nishimura, and Siebertz [13] and improves the recent quadratic kernel of Cranston, Mühlenthaler, and Peyrille [14]. For planar graphs, we further strengthen this upper bound to get a kernel of size at most 42k.

Keywords and phrases:
Reconfiguration, Independent Set, Kernel, Planar graphs
Funding:
Nicolas Bousquet: The author has been partly supported by ANR project ENEDISC (ANR-24-CE48-7768-01).
Copyright and License:
[Uncaptioned image] © Nicolas Bousquet and Daniel W. Cranston; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Mathematics of computing Discrete mathematics
Related Version:
Full Version: https://arxiv.org/abs/2506.03319 [10]
Editors:
Meena Mahajan, Florin Manea, Annabelle McIver, and Nguyễn Kim Thắng

1 Introduction

The combinatorial reconfiguration framework aims at investigating algorithmic and structural aspects of the solution space of an underlying base problem. For example, given an instance of some problem Π along with two feasible solutions Is and It, called the source and target feasible solutions, our goal is to determine if (and in how few steps) we can transform the source into the target via a sequence of adjacent feasible solutions. Such a sequence is called a reconfiguration sequence and every step in the sequence (going from one solution to an adjacent one) is called a reconfiguration step. Reconfiguration problems arise in various fields such as combinatorial games, motion of robots, random sampling, and enumeration. This framework has been extensively studied for various rules and types of problems in the last twenty years. The surveys [28, 29, 13] give a more complete overview of the field. In this paper we focus on transformation between independent sets; this study was initiated in [18] and motivated by planning motion of robots [19].

Given a simple undirected graph G, a subset of V(G) is independent if it induces no edges. Finding an independent set of maximum cardinality, i.e., the Independent Set problem, is a fundamental problem in algorithmic graph theory; it is known to be NP-hard. Furthermore, this problem is not approximable within a factor of O(n1ε), for any ε>0, unless P = NP [31].

We view an independent set as a collection of tokens placed on vertices, where no two tokens are adjacent. We can thus naturally define adjacency relations between independent sets, also called reconfiguration steps. In this paper, we focus on the Token Jumping (TJ) model, introduced by Kamiński et al. [23], where a single reconfiguration step consists of first removing a token on some vertex v and then immediately adding it back on any other vertex w, as long as no two tokens become adjacent. The token jumps from vertex v to vertex w. In the Token Jumping Independent Set Reconfiguration problem (abbreviated as ISRTJ), we are given a graph G and two independent sets Is and It of G. Our goal is to determine whether there exists a sequence of reconfiguration steps (called a reconfiguration sequence) that transforms Is into It.

The ISRTJ problem has received considerable attention. Hearn and Demaine proved that the problem is PSPACE-complete even restricted to planar graphs of maximum degree 3 [18] and Wrochna proved that it remains PSPACE-complete even restricted to graphs of bounded bandwidth [30]. On the positive side, this problem can be decided in polynomial time in certain restricted graph classes, such as line graphs and some of their extensions [9, 6] and even-hole free graphs (for this latter class, [23] gave a linear-time algorithm). The hardness of the problem motivates studying the problem from a parameterized perspective.

Parameterized Algorithms

A problem Π is FPT (fixed-parameter tractable) parameterized by a parameter x if there exists a function f and a polynomial P such that for every instance of Π of size n for which parameter x has value k, the problem can be decided in time f(k)P(n). One can easily prove that the existence of an FPT algorithm is equivalent to the existence of a kernel of size f(k) (for a function f), which is an algorithm that provides in polynomial time an equivalent instance111That is an instance which is positive if and only if the original instance is positive. of size f(k). A kernel is polynomial if f is a polynomial function.

Independent Set is known to be W[1]-complete [15] but admits FPT algorithms on Kr,r-free graphs and even semi-ladder-free graphs [16]. Its reconfiguration counterpart, ISRTJ also is W[1]-hard222Under some standard algorithmic assumptions, a W[1]-hard problem does not admit any FPT algorithm. parameterized by the size k of the independent sets [21]. One can naturally wonder what is happening when we restrict to structured graph classes. In [20], Ito et al. showed that the ISRTJ problem is FPT parameterized by k on planar graphs, and even on K3,r-free graphs (for each fixed value of r), i.e., graphs containing no copy of K3,r as a subgraph. This result has been generalized to nowhere dense graphs [26] and Kr,r-free graphs [12]. For a thorough history of parameterized aspects of independent set reconfiguration, we refer the reader to [13].

Although the parameterized behavior of ISRTJ is now well understood, its kernelization counterpart remains largely unexplored. Even beyond ISRTJ, a deep understanding of kernelization aspects of reconfiguration problems remains elusive (see the end of the present section). Although it was not mentioned in [20], their proof can be easily adapted to get a polynomial size kernel; for instance, see [12]. In a recent survey paper, Bousquet, Mouawad, Nishimura, and Siebertz [13] asked whether ISRTJ admits a linear kernel for all planar graphs. Cranston, Mühlenthaler, and Peyrille [14] proposed a quadratic kernel, even for graphs embedded on surfaces. In this paper, we answer affirmatively the question of [13] in a strong sense: we prove that ISRTJ admits a linear kernel when, for any positive integer r, we restrict to K3,r-minor-free graphs.

Theorem 1.

For each positive integer r, ISRTJ admits a kernel of size linear in k on K3,r-minor-free graphs.

The special case of planar graphs is of particular interest, so there we make the bound more precise.

Theorem 2.

For planar graphs, ISRTJ admits a kernel of size at most 42k.

The general approach that we use to prove each of these theorems is the same. But for planar graphs we work harder to optimize the multiplicative constant. We note that the kernel we output is actually a subgraph of the initial graph. This is, as far as we know, the first linear kernel for a reconfiguration problem. As a direct byproduct, we obtain an algorithm running in 2O(k)Poly(n) to decide ISRTJ. As far as we know, it is the first non-trivial single exponential algorithm for a reconfiguration problem. (At the end of this section, we discuss in more detail kernelization algorithms for other reconfiguration problems.)

To obtain a kernel that is linear, our argument must be more global than for the kernels in [20, 14]. Their proofs are based on a neighborhood decomposition argument. Let Is and It be the source and target independent sets and let X:=IsIt. They prove in [14] that the number of classes of neighborhoods in X is linear using the neighborhood diversity of planar graphs. It is easy to show that classes with at most 1 or at least 3 neighbors in X have linear size in total. The hardest part of the proof consists in reducing the size of classes with 2 neighbors in X. In [12, 20, 14], the proofs show that each class can be individually reduced to a linear number of vertices (in k). To get a linear kernel, we instead use a more subtle global approach, which is different from previous methods. We prove that we can keep only a constant number of vertices in each class and a linear number (in k) of well-chosen vertices in the union of the 2-classes and still preserve the existence of a solution. As far as we know, this is the first time that such a global argument has been used for reconfiguration.

In Sections 2.1 and 2.2, we explain in more detail the key ingredients needed to prove our Main General Theorem. And in Section 2.3 we complete its proof.

In the second part of this paper (Section 3), we explain how we can improve the kernel size in the specific case of planar graphs, to get a kernel of size 42k. This bound is probably still far from optimal, so we tried to compromise between a simple analysis and a small kernel size. As far as we know, no lower bound on kernel sizes has been proposed for reconfiguration. In the ISRTJ PSPACE-hardness proof of Hearn and Demaine [18, Theorem 23], the graph obtained in the reduction has size 3k and it seems very complicated to compact in polynomial time, so 3k appears to be a natural candidate for a lower bound. (In fact, their proof is written for ISRTS, but because their reduction uses only maximum independent sets, it works equally well for ISRTJ.) Proving a non-trivial lower bound, or improving further the upper bound to a single digit constant, remains a challenging open problem.

The existence of a polynmial kernel for ISRTJ on graph classes beyond K3,r-minor free graphs remains wide open. The proof of Bousquet, Mary, and Parreau [12] for Kr,r-free graphs generalizes the method of [20], but yields a kernel of size kf(r) where f is an exponential function. We thus ask the following question.

Question 3.

Does ISRTJ admit a kernel of size f(r)Poly(k) on bounded treewidth graphs? Kr-minor free graphs? On Kr,r-free graphs?

Related work: Kernelization and Reconfiguration

While the existence (and non-existence) of FPT algorithms for reconfiguration problems has recently been widely studied, almost no polynomial kernels have been proposed. Mouawad et al. [27] proved that TJ-Vertex Cover Reconfiguration and TJ-Feedback Vertex Set Reconfiguration both admit quadratic kernels. But the existence of linear kernels for these problems is still open. In the specific case of vertex cover, we note that this contrasts with the optimization setting, where in recent decades numerous classical linear kernels for vertex cover have been discovered; see e.g. [1].

As far as we know, our result gives the first non-trivial linear kernel for a reconfiguration problem. While the existence of a linear kernel for independent set is trivial in the optimization setting (every planar graph on at least 4k vertices is a yes-instance), the proof of Section 2 requires significant work. Many meta-kernelization algorithms guarantee the existence of linear kernels on planar graphs for optimization problems [2, 17], e.g., for dominating sets. While it remains open to determine whether TJ-Dominating Set Reconfiguration admits a linear kernel, several reduction rules of [2] cannot be adapted directly for reconfiguration.

Important machinery has been developed to prove that problems do not admit polynomial kernels, even if they admit FPT algorithms (AND and OR compositions for instance). As far as we know, this framework has never been used for reconfiguration problems. In particular, no problem is known to be FPT and also to not admit polynomial kernels. Finding such a problem, or finding a problem that admits a polynomial kernel for the optimization setting but not for its reconfiguration counterpart, remains interesting and open.

Finally, in this paper we focus on Token Jumping. Another model, called Token Sliding (TS), has been studied. In the TS model, tokens can only move along edges of the graph. Both problems remain PSPACE-complete on planar graphs and on graphs of bounded bandwidth [18, 30], but their complexities differ on many graph classes, such as chordal graphs [8] and bipartite graphs [25], where the sliding model is harder than its jumping counterpart. From a parameterized viewpoint, very little is known for the problem ISRTS. While ISRTJ is known to be FPT even on Kr,r-free graphs, the parameterized complexity of ISRTS is open even on graphs of bounded treewidth. ISRTS is known to be FPT on planar graphs [5] and on graphs with constraints on the girth [3, 4]. But the existence of polynomial kernels for ISRTS remains wide open. For dominating sets, the sliding version is much harder than its jumping counterpart since DSR-TS is XL-complete even on bounded treewidth graphs, but an FPT algorithm exists for DSR-TJ on planar graphs [11].

2 Main General Theorem

2.1 Proof Overview and Key Ideas

This section is devoted to proving our Main General Theorem. Fix a positive integer r. Fix an input graph G with no K3,r-minor, along with source and target independent sets, Is and It, each of size k. We will show that either ISRTJ(G,Is,It) is a trivial yes-instance, or else ISRTJ(G,Is,It) is equivalent to a problem ISRTJ(G,Is,It), where G is a subgraph of G and |V(G)|=O(k). Let X:=IsIt and note that |X|2k. The set X is called the set of key vertices. For each YX, the X-projection class 𝒞Y is defined by 𝒞Y:={vV(G)X s.t. N(v)X=Y}; the vertices of Y are the key vertices of 𝒞Y. Let

𝒞1:=YX|Y|1𝒞Y and 𝒞2:=YX|Y|=2𝒞Y and 𝒞3:=YX|Y|3𝒞Y.

For each integer k, we say that the X-projection class is a k-class if |Y|=k. In other words, 𝒞1 and 𝒞2 are the unions respectively of the 1-classes (and 0-class) and of the 2-classes, and 𝒞3 consists of all the other classes. So V(G)=X𝒞1𝒞2C3. Recall that |X|2k.

We will first bound the sizes of 𝒞1 and 𝒞3, in Section 2.2. As already observed in [20], if |𝒞1|χ(G)k, then ISRTJ(G,Is,It) is a yes-instance. However, computing χ(G) is hard. So we will work with a parameter χup(G) such that always χ(G)χup(G) and we can efficiently construct a χup(G)-coloring of G. (For example, for planar graphs we let χup(G):=4 and for general K3,r-minor-free graphs, we let χup(G):=r+2.) Thus, we assume |𝒞1|χup(G)k. Let N2(G):=|{YX:|Y|=2 and 𝒞Y}|, and let N3(G):=|{YX:|Y|3 and 𝒞Y}|. Since G has no K3,r-minor, for each Y with |Y|3, we have |𝒞Y|r1. So |𝒞3|(r1)N3(G). Thus, we aim to show that N3(G)=O(k). Proving this is easy for all planar graphs (and, more generally, for all graphs on each fixed surface), as we will show in Lemma 8. The proof for all K3,r-minor-free graphs is more challenging [22, 7]; but the result still holds (see Corollary 10).

So the core of the proof consists in showing that we can reduce the size of 𝒞2. If we could show that |𝒞2|=O(k), then we could take G as its own kernel. We cannot prove this directly, although it is true that N2(G) has size O(k). Yet, unsurprisingly, the size of a class 𝒞Y with |Y|=2 is in general not bounded by a constant. But can a 2-class be very large, say ω(k2)?

To motivate our approach in the remainder of the paper, we now sketch a crucial idea. Suppose that a 2-class 𝒞Y is very large for some 2-element set Y (large enough to necessarily contain an independent set of size kr). If it is impossible to ever, eventually, move a token to 𝒞Y starting from Is, then we can delete all of 𝒞Y without changing whether we can reconfigure Is into It, since no vertices of 𝒞Y can be used in a reconfiguration sequence. The same is true if it is impossible to ever eventually move a token to 𝒞Y starting from It. So we assume that neither of these is impossible.

Since 𝒞Y is very large, it contains an independent set IY of size kr. Since G is K3,r-minor-free, each vertex other than the two key vertices of 𝒞Y has at most r1 neighbors in 𝒞Y and, in particular, in IY. By assumption, starting from Is we can eventually move some token to 𝒞Y. In the resulting independent set I, both tokens have moved off the vertices of Y (moving the first of these tokens off is called unlocking 𝒞Y), so each of the k vertices with a token has at most r1 neighbors in IY. Thus, IY contains at least |IY|k(r1)=k vertices that are not adjacent to any vertex in I. So we can move all tokens on vertices in I to these available vertices of IY (in arbitrary order). The same is true starting from It. Finally, we can move tokens freely within IY, since it is an independent set. So we can reconfigure Is to It. This argument succeeds whenever |𝒞Y|χup(G)kr, since that guarantees an independent set IY of size kr (the largest color class in a χup(G)-coloring of G[𝒞Y]). Thus, whenever |𝒞Y|>χup(G)kr, we can delete arbitrary vertices of 𝒞Y.

Following this approach ensures333When G is K3,r-minor-free, it is straightforward to show that χ(G)=O(r). In fact, when r6300 Kostochka and Prince [24] showed that G is (r+2)-degenerate; thus, χ(G)r+3. See Lemma 5. that |𝒞2|χup(G)krN2(G)=O(k2). Below we adapt this idea to ensure that |𝒞2|max{O(χup(G)kr), O(N2(G))}. The main new idea is that the independent set IY above can be spread over multiple 2-classes. So if we unlock a 2-class containing vertices of IY, then we use its available vertices to receive tokens from the next 2-class containing vertices of IY, unlocking that one and proceeding by induction. Informally, G is formed from G by deleting some vertices of certain “big” 2-classes. Formally, we defer constructing G (and defining “big” 2-classes) to Construction 13. But once we define G we can prove the next lemma, which is the core of proving our Main General Theorem.

Lemma 4.

If in G we can (a) start from Is and unlock some big 2-class and also (b) start from It and unlock some big 2-class, then ISRTJ(G,Is,It) is equivalent to ISRTJ(G,Is,It), and both of them are yes-instances.

With Lemma 4 (formalized in Lemma 18), we prove our Main General Theorem as follows.

Proof of the Main General Theorem.

We show ISRTJ(G,Is,It) and ISRTJ(G,Is,It) are equivalent. Since GG, if the latter is a no-instance, then so is the former. So assume instead that ISRTJ(G,Is,It) is a yes-instance. First, suppose that it is impossible, starting from Is, to ever unlock a big 2-class. So all big 2-classes will always remain locked, and it is impossible to ever move a token to a vertex of a big 2-class. Thus, since every vertex of V(G)V(G) is in a big 2-class, every reconfiguration sequence in G, starting from Is is also valid in G. This proves the desired result. The argument is identical if it is imposible to unlock a big 2-class, starting from It. Thus, we assume instead that, starting from both Is and It, it is possible to unlock some big 2-class. Now we are done by Lemma 4.

2.2 Dealing with 𝓒𝟏 and 𝓒𝟑

In this subsection, we determine a function f such that if G is K3,r-minor-free, then we can assume that |𝒞1|+|𝒞3|f(r)k. And when G is planar, we can improve our bound on f. Bounding |𝒞1| is easy, both in the planar case and in the more general case. But bounding |𝒞3| is more work. For this we use the observation (Lemma 7) that |𝒞3|(r1)N3(G); recall here that N3(G) denotes the number of sets YX with |Y|3 and 𝒞Y. To bound N3(G) in the general (non-planar) case, we use a powerful result (Lemma 9) from [7].

It is straightforward to prove that if G is K3,r-minor-free, then χ(G)=O(r). But determining the right multiplicative (and additive) constant is more work. This is done by the following lemma, which is sharp.

Lemma 5 (Kostochka–Prince [24]).

Fix r6300. If G is an n-vertex graph with nr+3 and G has no K3,r-minor, then 2|E(G)|(r+3)(n2)+2. Thus, G is (r+2)-degenerate and χ(G)r+3.

Lemma 6.

If |𝒞1|χup(G)k, then ISRTJ(G,Is,It) is a yes-instance. In particular, since G is K3,r-minor-free, this is true whenever r6300 and |𝒞1|k(r+3).

Proof.

The second statement follows from the first by Lemma 5; thus, we prove the first.

Assume |𝒞1|χup(G)k. By definition, G is χup(G)-colorable, and we can compute such a coloring efficiently. By Pigeonhole, 𝒞1 contains an independent set Im (for middle) of size χup(G)k/χup(G)=k. Starting with tokens on Is, for each vIs with a neighbor wvIm, move the token on v to some such wv. Now move all remaining tokens (in an arbitrary order) to the unoccupied vertices of Im. By symmetry, we can also move all tokens from It to Im. Thus, we have a yes-instance of ISRTJ(G,Is,It), as claimed.

Henceforth we assume |𝒞1|<χup(G)k. Recall from above that N3(G) denotes the number of sets YX with |Y|3 and 𝒞Y. The next lemma follows directly from the fact that G is K3,r-minor-free.

Lemma 7.

|𝒞3|(r1)N3(G).

Proof.

Suppose the lemma is false. By Pigeonhole, there exists YX with |𝒞Y||𝒞3|/N3(G)>(r1)N3(G)/N3(G). That is, |𝒞Y|r. But now G contains the subgraph K3,r with the vertices in the part of size 3 in Y and those in the part of size r in 𝒞Y. This contradicts that G is K3,r-minor-free.

When G is planar, we can use Euler’s formula to improve the bound above.

Lemma 8.

If G is planar, then N2(G)3|X|6k and N3(G)2|X|4k.

Proof.

We draw a plane graph GX with vertex set X where each set YX with |Y|=2 and CY corresponds to an edge of GX. Think of restricting G to X and one vertex vY in CY for each such Y with |CY|=2 (deleting any edges among vertices of X). For each vY, we now contract exactly one of its two incident edges. Note that for the resulting plane graph GX its number of edges is precisely |N2(G)|, the number of 2-classes of G. By Euler’s Formula, GX has at most 3|X|6 edges, so N2(G)3|X|6k.

For every class C in 𝒞3, we choose one representative xC (recall that xCX). We denote by p the number of such classes, i.e., p:=|N3(X)|. We now construct the bipartite graph with parts X and {xC:C𝒞3}; that is, we consider the subgraph induced by these vertices, but with all edges removed that have both endpoints inside X or have both endpoints inside {xC:C𝒞3}. The resulting graph is indeed a bipartite planar graph. By Euler’s formula, the number of edges of a bipartite planar graph is at most twice its number of vertices. So we have 3p<2(p+|X|)2(p+2k), which implies that p<2|X|4k, as claimed.

The neighborhood complexity of a graph class 𝒢 is the smallest function f, if it exists, such that for all G𝒢, nonempty AV(G), and nonnegative integers r, we have the bound |{Ns[v]A:vV(G)}|f(s)|A|. Here Ns[v] is the set of vertices at distance at most s from v. We need the following result.

Lemma 9 ([7, Theorem 18]).

For all positive integers s,t with t4, for every Kt-minor-free graph G, for every set A of vertices of G,

|{Ns[v]A:vV(G)}|4t(t3)t2(t1)(s+1)3(t1)|A|.

Since we are interested only in neighborhoods (that is, distance 1), we let s:=1. Since G is K3,r-minor-free, it is also K3+r-minor-free. So we let t:=r+3. Finally, we let A:=X and recall that |A|=|X|2k.

Corollary 10.

N2(G)+N3(G)4r+3r(r+3)2r+423r+6(2k)25r+13(r+3)2r+5k.

Combining the results in this subsection, we get the following.

Lemma 11.

|𝒞1|+|𝒞3|k(max{r,6300}+3+(r1)(25r+13(r+3)2r+5)). If G is planar, then |𝒞1|+|𝒞3|12k.

Proof.

We start with the first statement. If G is K3,r-minor-free, with r6300, then also G is K3,6300-minor-free. So the bound on |𝒞1| follows from Lemma 6, and the bound on |𝒞3| follows from Lemma 7 and Corollary 10. Summing these bounds gives the first statement.

Now we prove the second statement. By Lemma 6, we assume that |𝒞1|4k. By Lemma 7 (with r:=3) and Lemma 8 we get that |𝒞3|(31)(4k)=8k. Summing these bounds gives the second statement.

2.3 Bounding the size of 𝓒𝟐

The rest of the proof consists in showing that the following lemma holds.

Lemma 12.

We can find in polynomial time an equivalent instance, formed by possibly deleting some vertices of 𝒞2, to get a subset 𝒞2 for which |𝒞2|χup(G)(N2(G)(4r1)+k).

First we construct our graph G, by (possibly) deleting some vertices of 𝒞2, and we show that |𝒞2|χup(G)(N2(G)(4r1)+k). This is fairly straightforward. Afterwards, we show that ISRTJ(G,Is,It) is equivalent to ISRTJ(G,Is,It). We sketched this latter step above. So all that remains for us is to handle the harder case: when it is possible starting from Is to move a token to some vertex of 𝒞2𝒞2, and this is also possible starting from It.

Construction 13.

To form G from G, we do the following.

  1. (1)

    If |𝒞2|χup(G)(N2(G)(3r2)+k), then do nothing; that is, 𝒞2:=𝒞2.

  2. (2)

    Otherwise, by Pigeonhole pick I𝒞2 such that I is an independent set and |I|=N2(G)(3r2)+k.

  3. (3)

    A 2-class 𝒞Y is big if |𝒞Y|χup(G)(2r1)+1; otherwise, 𝒞Y is small.

  4. (4)

    For each small 2-class, do nothing.

  5. (5)

    For each big 2-class 𝒞Y, do the following.

    1. (a)

      If 𝒞Y has at least 3r1 vertices of I, then delete all vertices of 𝒞YI.

    2. (b)

      If 𝒞Y has at most 3r2 vertices of I, then:
      Keep in 𝒞Y an arbitrary independent set of size 2r and remove all other vertices of 𝒞Y.
      Remove all the vertices of I𝒞Y from I.

Proposition 14.

We have |𝒞2|χup(G)(N2(G)(4r1)+k).

Proof.

If |𝒞2|χup(G)(N2(G)(4r1)+k), then we are done, trivially. So assume not. Now we define I and delete vertices of big 2-classes as in Construction 13. If a 2-class 𝒞Y is either small or intersects I in at most 3r2 vertices, then in 𝒞2, we keep at most χup(G)(2r1) vertices of 𝒞Y. Thus, the total number of vertices in these classes (restricted to G) is at most N2(G)χup(G)(2r1).

If a 2-class 𝒞Y is big and intersects I in at least 3r1 vertices, then in G we keep in 𝒞Y only its vertices in I. Thus, the total number of vertices in these classes (restricted to G) is at most |I|N2(G)(3r2)+k. So the total size of 𝒞2 is at most N2(G)χup(G)(2r1)+N2(G)(3r2)+kχup(G)(N2(G)(4r1)+k).

A helpful independent set is any subset of I of size k. Since I is independent, the following is clear.

 Remark 15.

Any helpful independent set can be transformed into any other.

For a 2-class 𝒞Y, we call the 2 vertices in Y the key vertices of 𝒞Y. To unlock a big 2-class 𝒞Y is to move tokens to reach an independent set I such that |IN(𝒞Y)|1. After we unlock a class 𝒞Y, we can move the single token in N(𝒞Y), if it exists, onto 𝒞Y and then move additional tokens onto 𝒞Y (provided that 𝒞Y contains a large enough independent set).

Lemma 16.

Fix x,xX and let C be the {x,x}-class. Every component of G[V(C{x,x})] is adjacent to at most r1 vertices of C.

Proof.

If not, then G contains a K3,r-minor, where one side consists of the vertices x,x, and the component A with r neigbhbors in C, and the other side consists of r vertices of N(A)C.

 Remark 17.

Having defined G and bounded its size, all that remains is to prove that this new instance ISRTJ(G,Is,It) is equivalent to the original ISRTJ(G,Is,It). This equivalence is precisely the assertion of Lemma 4, and it follows immediately from Lemma 18.

Lemma 18.

If in G we can from Is (resp. It) unlock a big class, then in G we can from Is (resp. It) reach a helpful independent set. That is, there exists a transformation from Is (resp. It) into a helpful independent set in G that only uses vertices of G.

Proof.

Assume that there exists an independent set J0 that can be reached from Is such that |N(C)J0|=1 for some big class C. Among all transformations from Is to J0, take a transformation of minimum length. The case when has length 0 (that is J0=Is) is easier, so we handle it briefly at the end. For now we assume that has positive length.

We claim that: (i) the last step of consists in moving a token on a key vertex x of class C to some vertex z and, (ii) each jump of the transformation except the last one consists of moving a token (from its current vertex) to an adjacent vertex. Point (i) follows from the minimality of the transformation. At some step in , we move a token away from a key vertex of some big 2-class. If continues with further steps, then we can omit these steps, contradicting the minimality of . Point (ii) holds because if, prior to the last step in , we moved a token from a vertex v to a vertex w, with w not adjacent to v, then we should have instead moved the token on the key vertex x to w; this gives a shorter transformation, again contradicting the minimality of .

We form G′′ from G by deleting all vertices in big classes and all key vertices for big classes. Now (ii) above implies that all vertices that have gained or lost a token during must be in the same component of G′′; otherwise we can simply omit from all moves in components of G′′ other than the component where we move our token on our final move, which unlocks C.

We claim that each big class C has at most 2(r1) vertices with neighbors in J0Is, as follows. Let z denote the vertex where we move a token on the final step of (unlocking C). Note that all vertices of J0(Is{z}) belong to the same component of G′′; this follows from the previous paragraph, since each of these vertices received a token during (excluding its last step). Lemma 16 ensures that each big class C has at most r1 vertices with neighbors in J0(Is{z}). Lemma 16 also ensures that each big 2-class C has at most r1 neighbors of z. Thus, the claim holds.

By the definition of J0, there is a big 2-class C that has been unlocked; that is J0 only contains a token on at most one of the two key vertices of C. Regardless of whether or not C contained (in G) at least 3r1 vertices of I, we know that C contains in G an independent set of size at least 2r. From above, at most 2r2 of these vertices have neighbors in J0Is. So at least 2r(2r2)=2 of them have no such neighbor. We denote by {a,b} an independent set of size 2 in CN(J0Is).

By Lemma 16, each of a,b has at most r1 neighbors in each other big class C. Actually, let x,y denote the key vertices for the big 2-class C that contains a,b. For each big class C, with CC, one of x and y is not a key vertex for C; by symmetry, we assume x is not. If |N({a,b})C|r, then we get a K3,r-minor by contracting {a,b} onto x (with x and the two key vertices of C as the part of size 3). Thus, |N({a,b})C|r1. So for each class C containing at least 3r1 vertices of I, we know that |(CI)N({a,b}(J0X))|(3r1)(r1)(r1)(r1)=2. We denote by I the set IN({a,b}). Note in particular that by construction I contains at least 2 vertices in each big 2-class that contains (in G) vertices of I.

Let C1,,C be the big classes intersecting I in G (different from C if C also intersects I). Let b be a vetex of IC1 and x1 be a key vertex of C1. Note that no vertex of J0 is adjacent to b. So we can move the token444If x1=x, then we have already moved the token that was on x1, so we now do nothing. on x1 to b to reach an independent set that we denote by J0. Note that C1 is unlocked in J0 (since |N(C1)X|=1.) We can now unlock all the big classes C1,Ci,,C intersecting I in G, by induction.

At the end of this transformation, we get an independent set J where every big class containing vertices of I has been unlocked. Moreover, by construction, vertices of JI have at most 3r3 neighors in each big class. When constructing G from G, we deleted vertices of I only in step (5b). So the number of vertices of I we deleted is at most N2(G)(3r2). Thus, the total number of vertices in I (in G) that are unavailable in G to receive tokens from J is at most N2(G)(3r2). So the number of vertices available to receive tokens, from J is at least |I|N2(G)(3r2)=k. Hence, in G all the tokens of J can be moved to I, as desired.

Now we remark briefly on the case that has length 0; that is, some big 2-class C is already unlocked. In this case, we just begin immediately moving tokens to a,b in C. Now each big 2-class C has at most r1 vertices in N({a,b}), but we do not need to worry about neighbors of z in the component of G′′ where other moves occurred. So the analysis above still holds.

3 Improved Kernel for Planar Graphs

3.1 Proof Outline

Now we provide an algorithm that outputs a smaller kernel in the specific case of planar graphs. More precisely, the goal of Section 3 is to prove the following result.

Theorem 19.

On planar graphs ISRTJ admits a kernel of size 42k.

The general idea of the proof is similar to that of Lemma 18. We prove that if G is large enough, then G contains an independent set I of size at least k with the following property: if we can unlock one of the “big classes” from Is (or It), then we can transform Is (or It) into a size k subset of I. To obtain a smaller kernel, we need two main ingredients. First, we give a more subtle way to define this independent set I. This allows us to find such an independent set I in planar graphs much smaller than required by Lemma 18. Second, we prove that, as in Lemma 18, if we can unlock one class that is big enough, then we can transform Is into I. The difference from Lemma 18 is that our new notion of “big enough” is actually much smaller; but this savings comes at the cost of slightly more involved analysis.

We now explain in more detail how we find this set I and describe some of its properties. Let X:=IsIt. Let Y be a subset of vertices. The X-neighborhood of Y is yYN(y)X. One of the key ideas in the proof is the concept of (weakly) greedy independent sets. Let I be an independent subset of VX consisting of vertices in 2-classes.

Figure 1: An Is-greedy independent set, together with the transformation Refer to caption .
Figure 2: A weakly Is-greedy independent set. Refer to caption are the activation pairs and is the tranformation.

We say that I is Is-greedy (resp. It-greedy) if there is a greedy algorithm that moves tokens (one-by-one) from the vertices of Is (resp. It) onto vertices of I, while keeping an independent set all throughout the transformation; see Figure 1. To rephrase, this means that, at each step of the transformation, we can find a vertex of IsI that can be replaced by a vertex of IIs. Again equivalently, but in a more structural way, there is an ordering i1,,ik of Is and j1,,jk of I such that, for every tk, vertices j1,,jt,it+1,,ik form an independent set. Note, if I1 and I2 are both independent sets of size k, that I1 is I2-greedy if and only if I2 is I1-greedy. That is, being greedy is symmetric.

We will also need the following weakening of Is-greedy independent sets. The set I is weakly Is-greedy if there exist vertices i1,i2Is and j1,j2I such that (Is{i1,i2}){j1,j2} is an independent set, call it I, and I is I-greedy; see Figure 2. In other words, there are orderings i1,,ik of Is and j1,,jk of I such that, for every t{2,,k}, the vertex subset {j1,,jt,it+1,,ik} is independent. Note that an Is-greedy independent set is indeed weakly Is-greedy. If I is weakly Is-greedy, then the pair {i1,i2} (resp. {j1,j2}) is called the Is-activation pair (resp. I-activation pair). These pairs “activate” the transformation in the sense that, if {i1,i2} has been replaced by {j1,j2}, then we can greedily finish the transformation from Is into I. (In the rest of the proof, I might have size larger than k, since we simply want to transform Is into a subset of I; but imagining that these set sizes are equal keeps all the hardness of the problem.)

Assume that G contains an independent set I of size at least k that is weakly Is-greedy and weakly It-greedy. If I is Is-greedy and It-greedy, then we can transform Is into It, passing through I. But if I is only weakly Is-greedy, then nothing ensures that we can transform Is into I. Nevertheless, by definition, if we can replace the activation vertices of Is with the activation vertices of I (and similarly for It), then we can transform Is into I. But (i) there might not exist any transformation between Is and I and, (ii) if there is a transformation, nothing guarantees that some such transformation satisfies this condition.

To overcome point (ii), we exhibit certain special weakly greedy independent sets (called weakly clean independent sets) in Section 3.3. And we also prove that if G is large enough, then either there is no transformation from Is into I or there is a transformation that replaces the activation pair of Is by the activation pair of I without moving the other tokens. Moreover, in the latter case, this transformation only uses a constant number of vertices in each 2-class, as well as (possibly) vertices in classes that are not 2-classes. (We optimize this constant knowing that the graph is planar.) Finally, we prove that every planar graph that is large enough has an independent set I that is both weakly Is-greedy and weakly It-greedy; this completes the proof.

Organization

In Section 3.2, we start with a few observations. In Section 3.3, we define clean independent sets, and prove that if G is large enough, then it contains an independent set I that is clean both for Is and for It. In the longer version [10] we combine all these arguments to get the desired smaller kernel, assuming the truth of a key lemma about transformations of Is into I; finally, we conclude by proving this key lemma.

3.2 First Observations

When constructing a sequence to reconfigure Is to It, we would prefer to be able to move tokens onto vertices of distinct 2-classes independently of each other. But this may be impossible, because of edges between some of these vertices. So this first subsection is about how we can allow ourselves this desired freedom. Throughout this section, we extensively use the following simple remark; it is due to the fact that all vertices belonging to a given 2-class are adjacent to the same two key vertices.

 Remark 20.

If G is a planar graph, then the vertices of each 2-class C induce either a cycle or a disjoint union of paths.

Proof.

If some vertex v in C has at least 3 neighbors in C, say w1,w2,w3, then G contains as a subgraph K3,3, with {w1,w2,w3} in one part and with v and the key vertices for C in the other. This K3,3 contradicts that G is planar. And if C induces a cycle H, then H separates the key vertices, so no other vertex is adjacent to both key vertices. Thus, H spans C.

In particular, every 2-class C contains an independent set of size |C|/2. Moreover, if we consider a strict subset of C, that is a set DC, then D induces a disjoint union of paths; so D admits an independent set of size at least |D|/2. The subset D is typically formed from C by deleting 2 vertices. We usually remove vertices to guarantee that 2-classes are anticomplete to each other (disjoint vertex subsets A and B are anticomplete to each other if no edge of G has one endpoint in A and the other in B). Namely, we often use the following remark.

 Remark 21.

If C,C are distinct 2-classes, then the following 2 statements hold.

  1. (a)

    The set N(C)C has size at most 2. Moreover, if N(C)C has size 2 then its two elements must be either (i) consecutive vertices on a path of G[C]; or (ii) two endpoints of a single path of G[C]; or (iii) two endpoints of disjoint paths of G[C].

  2. (b)

    If x,y are the key vertices of C, then, for every zX distinct from x,y, the union of the classes incident to z has at most two neighbors in C. (Otherwise, G has a K3,3-minor.)

In the case of planar graphs, we can actually strengthen Remark 21(a) to apply to more than two classes, and we prove the following version.

Lemma 22.

Let G be a planar graph. Let C1,,Cr be 2-classes. For every i<r, there exist CiCi with |Ci||Ci|2 such that C1,,Cr1,Cr are pairwise anticomplete.

Proof.

Consider a plane drawing of the subgraph of G induced by irCiX such that the outer face of the drawing contains either the 2 key vertices of Cr or 2 vertices contained in Cr, possibly both. For every class Ci, let Gi denote the subgraph induced by Ci and its 2 key vertices. A class Ci is nested in Cj if all the vertices of Ci lie within a face of Gj distinct from its outer face. Two classes Ci and Cj are incomparable if the vertices of Ca are on the outer face of Gb whenever {a,b}={i,j}. For every 2-class C, the vertices of C plus its two key vertices form a K2,|C| (possibly with extra edges among C), so every pair of classes is either nested or incomparable.

Now, for every i<r, let Bi denote the at most two vertices of Ci on the boundary of Gi, and let Ci:=CiBi. We claim that, Ci is anticomplete to Cj for every ji (with Cr:=Cr). If the two classes are incomparable, then the only vertices of Ci that can be adjacent to Cj are the vertices of Bi and Bj, but the vertices of at least one of these sets are excluded from CiCj (and the vertices of both are if r{i,j}). Otherwise, up to symmetry, Ci is nested in Cj. By the definition of the plane drawing, ir and the vertices of the outer face of Ci are excluded from 𝒞i.

Later we will need a slight variation of Lemma 22. (Its proof is nearly identical to that above.)

Lemma 23.

Let G be a planar graph, and let C1,C2 be two 2-classes. If C is the vertex set of a connected subgraph of G[V(C1C2X)], then the following 2 statements hold.

  1. (a)

    There exist C1,C2 such that |Ci||Ci|2 for every i{1,2} and C1,C2,C are pairwise anticomplete.

  2. (b)

    If C1 and C2 are anticomplete, then there exists CC such that |C||C|4 and C,C1,C2 are pairwise anticomplete.

3.3 Clean Independent Sets

We define two types of independent sets; these are similar to the helpful independent sets of Section 2. A weakly Is-greedy independent set I is 3-clean for Is if it satisfies both of the 2 conditions below; see Figure 4.

  1. 1.

    Some 2-class C contains at least three vertices of I and,

  2. 2.

    The vertices of the activation pair555There might exist several orders, and several activation pairs. In this case, we select one pair (and corresponding order) that satisfies the condition and call this the activation pair. of I are in C.

A weakly Is-greedy independent set I is (2,2)-clean for Is if it satisfies both of the 2 conditions below; see Figure 4.

  1. 1.

    Some vertex x is a key vertex for two 2-classes C,C that both contain two vertices of I and,

  2. 2.

    The vertices of the activation pair of I are the vertices of CI.

Figure 3: The independent set I is 3-clean for Is. are the activation pairs.
Figure 4: The independent set I is (2,2)-clean for Is. are the activation pairs.

The set I is weakly clean for Is if it is either 3-clean or (2,2)-clean. Note that if I is weakly clean for Is, then the Is-activation pair is in N(C)X. The key vertices of C (and C for (2,2)-clean independent sets) might not be in Is. But in that case, we can easily transform Is into I, as we show in the next lemma.

Lemma 24.

Let I be a 3-clean (resp. (2,2)-clean) independent set for Is. If at least one of the two vertices of N(C)X (resp. N(CC)X) is not in Is then we can transform Is into I using only vertices of IsI.

Proof.

Since I is Is-weakly greedy, we simply need to prove that we can move the tokens from the activation pair of Is to the activation pair of I. Indeed, if we can do this, then afterward we can complete the transformation, from Is into I, greedily.

First suppose that I is 3-clean. By definition, the activation pair {i1,i2} of I is in C and the activation pair {j1,j2} of Is contains the vertex j1 of N(C)Is (if it exists). We can replace666By replace a with b we mean to move the token from vertex a to vertex b; this replaces a with b in the independent set defined by the vertices currently with a token. j1 with i1 and replace j2 with i2 (since N(C)Is has at most one vertex, namely j1) and the conclusion follows.

Now instead assume that I is (2,2)-clean. If |N(C)X|1, then we can perform the same moves and the conclusion follows similarly. So we assume that the activation pair of Is is {x,j2}, the two vertices of N(C)X. By assumption not all the vertices of N(CC)X are in Is. So the second key vertex of C is not in Is. Thus, we can replace x with a vertex y of IC, replace j2 with i1, and finally replace y with i2. This completes the proof.

From now on, by Lemma 24, we assume that the classes C and C are locked, that is N(C)Is and N(C)Is each have size 2. In particular, the activation pair of I is N(C)Is. If I is 3-clean (resp. (2,2)-clean), then the vertex of IC (resp. the two vertices of IC) that is not in the I-activation pair (if several such vertices exist, then we choose one of them arbitrarily) is called the auxiliary activation vertex (resp. vertices) of I. Moreover, if I is (2,2)-clean, then the key vertex of C that is not a key vertex of C is called the auxiliary activation vertex of Is.

An independent set is clean for Is if it is Is-greedy, 3-clean for Is, or (2,2)-clean for Is. As we already mentioned, it is weakly clean if it is clean but not Is-greedy. And if I is Is-greedy, then we can transform I into Is. We now explain informally how we will make use of an independent set that is 3-clean or (2,2)-clean for Is. We will argue that if we can unlock a large enough class D, then (if we consider a good transformation) we can replace an X-vertex adjacent to C (resp. replace x, when I is (2,2)-clean) with a vertex in the class D. This fact, together with the fact that CI (resp. (CC)I) is large enough will allow us to find a transformation from Is into I. We formalize this intuition with the following simple example.

Lemma 25.

Let G be a K3,3-free graph, and let an independent set I be clean for Is. If |V(G)N(Is)|3, then we can transform Is into I using at most one vertex that is not in IsIt.

Proof.

The conclusion follows if the independent set I is Is-greedy. So we instead assume that I is weakly Is-greedy. Thus, if we can replace the activation pair of Is by the activation pair of I, then we can complete the transformation greedily. The rest of the proof consists in showing that we can do this. Let {i1,i2} be the I-activation pair and i3 be an auxiliary I-activation vertex (and i4 be the other if I is (2,2)-clean). We denote by C the class containing i1,i2 (and by C the class containing i3 if I is (2,2)-clean). Since G is K3,3-free, there is a non-edge between some vertex aV(G)N(Is) and some vertex ib of {i1,i2,i3}.

If I is 3-clean, then we let {j1,j2} be the activation pair of Is (these are also the key vertices of C). We replace j1 with a, replace j2 with ib, replace a with some vertex ic in {i1,i2}ib, and finally replace ib with {i1,i2}ic if ib=i3.

If I is (2,2)-clean, then we let {x,j1} be the Is-activation pair, and let j2 be the vertex of N(C)Is distinct from x. If ib is in the I-activation pair, then we can conclude as above for 3-clean independent sets. So we assume that ib=i3. We replace x with a, replace j2 with i3, replace a with i4, replace j1 with i1, replace i3 with i2, and replacer i4 with j2.

To conclude this section, we prove that if G is large enough, then the graph contains an independent set that is clean for both Is and It. Namely, the following holds.

Lemma 26.

Let G be a planar graph and Is,It be two independent sets of size k. If the number of vertices in 2-classes is at least 21k, then there exists an independent set I of size at most 2k that is both clean for Is and clean for It.

Proof.

Note that if an independent set I is weakly Is-greedy (resp. It-greedy), then I remains so when we add vertices to I. So it suffices to find an independent set I of size k that is clean for Is, find another I′′ for It, and take their union, as long as this union is also independent. To ensure this union is indeed independent, we first find a large independent set I0 and choose the clean independent sets I and I′′ from within I0.

Let X:=IsIt. By Lemma 8, the number of 2-classes in G is at most 3|X|6k. For each 2-class C, we call C a good class for Is (resp. for It) if C has at most one neighbor in Is. A class that is not good is called bad.

By Lemma 22, after removing at most 2 vertices per class, we assume that all the 2-classes are anticomplete to each other. The total number of vertices that we remove is at most 2|N2(X)|6|X|12k. Moreover, by Remark 20, the remaining vertices of each 2-class induce a disjoint union of paths, so each contains an independent set with at least half its vertices. We denote by I0 the union of these independent sets; note that I0 is also independent and |I0|(21k12k)/2=4.5k. As we mentioned above, we choose from among vertices of I0 an independent set of size k that is clean for Is, and we do the same for It; the union of these two sets is the desired independent set I, with size at most 2k. So below it suffices only to construct the independent subset of I0 that is clean for Is.

If the number of vertices of I0 appearing in good classes for Is is at least k, then we take an arbitrary set of k of them; this set is Is-greedy, so we are done. Thus, we assume instead that the number of vertices of I0 in good classes is at most k1. Hence, the number of vertices of I0 that are in bad classes is at least 4.5k(k1)=3.5k+1.

If more than 0.5k bad classes each have at least 2 vertices in I0, then by Pigeonhole 2 such bad 2-classes C,C share a key vertex of Is. To form an independent set that is (2,2)-clean for Is, we take the union of the vertices of I0 in C,C, and then continue picking a bad class (with at least two vertices of I0) and adding all of its vertices, until we reach a set of size k. To see that this set is (2,2)-clean, we start the ordering with the vertices of CC.

So we assume instead that all but at most 0.5k bad 2-classes contain at most 1 vertex of I0; we call them small classes. We denote by the number of non-small bad classes; thus, 0.5k. By the proof of Lemma 8, at most 3k classes are bad; so the number of small bad classes is at most 3k. Thus, the number of vertices of I0 in small bad classes is at most 3k. So the number of vertices of I0 in non-small bad classes is at least 3.5k+1(3k)=0.5k++12+1, since 0.5k. Thus, by Pigeonhole, some bad 2-class C has at least 3 vertices of I0. To form our 3-clean independent set (for Is), we take all of the vertices of I0 in C and add vertices of I0 from bad 2-classes that are not small, up to a set of size k (or when no remaining bad class has two vertices of I0).

To order I, we begin with the vertices in a class of size at least 3, and continue to other bad 2-classes, always making all vertices in a bad 2-class successive in the order. If we reach a set of size k, then this independent set I is weakly Is-greedy, and we are done; so we assume we do not reach a set of size k.

However, we do reach a set I of size at least 0.5k++1. So we need to add to I at most 0.5k1 vertices. After moving in the tokens from all key vertices needed to unlock I, these 0.5k++1 vertices of I have at least 0.5k++12=0.5k+1 vertices with no token. Thus we can move to them one token from each of 0.5k1 small bad classes. Afterwards, all of these small bad classes are unlocked, so the remaining tokens can move to these vertices. Thus, the resulting set is weakly Is-greedy.

Corollary 27.

In fact, if there exist q 2-classes that each have a key vertex outside of Is, and the union of these classes has size at least 2q+2k, then Is is I-greedy.

Proof.

By Pigeonhole, one of these classes C has size at least (2q+2k)/q3. Recall that C is Is-unlocked, since C has a key vertex outside of Is. So we can start by replacing the first 3 vertices of Is with these 3 vertices of C. The proof that we can finish the desired order of I is precisely the final 2 paragraphs of the previous proof.

Due to space constraints, the proof of this statement is not included in the extended abstract but can be found in the long version [10]. The proof that the kernel has the desired size is centered around the following definition and technical lemma. A vertex is important if it is a key vertex for either (a) at least one 2-class of size at least 7 or (b) at least two 2-classes of size at least 5.

Lemma 28.

Let G be a planar graph and I be an independent set that is weakly clean for Is. We can transform Is into an independent subset of I whenever we can unlock from Is either (a) a 2-class of size at least 7 or (b) a 2-class of size at least 5 with a key vertex adjacent to a second 2-class of size at least 5. Moreover, such a transformation still exists in every subgraph G formed from G by deleting vertices (outside of I) in 2-classes of size at least 5 such that every vertex important in G remains important in G.

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