Abstract 1 Introduction 2 Preliminaries 3 Hardness 4 FPT Algorithms 5 Conclusion References

Structural Parameterizations of 𝒌-Planarity

Tatsuya Gima ORCID Hokkaido University, Sapporo, Japan Yasuaki Kobayashi ORCID Hokkaido University, Sapporo, Japan Yuto Okada ORCID Nagoya University, Japan
Abstract

The concept of k-planarity is extensively studied in the context of Beyond Planarity. A graph is k-planar if it admits a drawing in the plane in which each edge is crossed at most k times. The local crossing number of a graph is the minimum integer k such that it is k-planar. The problem of determining whether an input graph is 1-planar is known to be NP-complete even for near-planar graphs [Cabello and Mohar, SIAM J. Comput. 2013], that is, the graphs obtained from planar graphs by adding a single edge. Moreover, the local crossing number is hard to approximate within a factor 2ε for any ε>0 [Urschel and Wellens, IPL 2021]. To address this computational intractability, Bannister, Cabello, and Eppstein [JGAA 2018] investigated the parameterized complexity of the case of k=1, particularly focusing on structural parameterizations on input graphs, such as treedepth, vertex cover number, and feedback edge number. In this paper, we extend their approach by considering the general case k1 and give (tight) parameterized upper and lower bound results. In particular, we strengthen the aforementioned lower bound results to subclasses of constant-treewidth graphs: we show that testing 1-planarity is NP-complete even for near-planar graphs with feedback vertex set number at most 3 and pathwidth at most 4, and the local crossing number is hard to approximate within any constant factor for graphs with feedback vertex set number at most 2.

Keywords and phrases:
1-planar graphs, local crossing number, beyond planarity, parameterized complexity, kernelization
Funding:
Tatsuya Gima: JSPS KAKENHI Grant Number JP24K23847 and JP25K03077.
Yasuaki Kobayashi: JSPS KAKENHI Grant Numbers JP23K28034, JP24H00686, and JP24H00697.
Yuto Okada: Supported by JST SPRING, Grant Number JPMJSP2125.
Copyright and License:
[Uncaptioned image] © Tatsuya Gima, Yasuaki Kobayashi, and Yuto Okada; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Theory of computation Parameterized complexity and exact algorithms
Related Version:
Full Version: https://arxiv.org/abs/2506.10717
Acknowledgements:
We would like to thank Alexander Wolff for valuable discussions in Würzburg and anonymous reviewers for insightful comments.
Editors:
Vida Dujmović and Fabrizio Montecchiani

1 Introduction

Graph drawing is recognized as an important area in the research of graph theory and algorithms due to various real-world applications. In particular, efficient algorithms for drawing graphs on the plane without edge crossings have received considerable attention from various perspectives. However, to visualize real-world networks, it is necessary to consider drawing non-planar graphs in many cases. This research direction is positioned as Beyond Planarity [8, 11, 21], and a significant amount of effort has been dedicated to analyzing their graph-theoretic properties and designing algorithms for drawing these graphs. In this context, the problem of drawing an input graph on the plane with the minimum number of crossings is one of the most well-studied problems. In other words, the problem asks for the crossing number of an input graph, which is known to be NP-hard [15]. Among various studies on this problem, the (recent) progress on the parameterized complexity of Crossing Number, where the goal is to determine whether an input graph G can be drawn in the plane with at most k crossings, is remarkable [7, 18, 23, 27]. In particular, this problem is fixed-parameter tractable (FPT) when parameterized by k, that is, it admits an algorithm with running time f(k)nO(1), where n is the number of vertices in the input graph and f is a computable function.

The local crossing number is one of the well-studied variations of the standard crossing number [1, 2, 4, 17, 20, 26, 31, 33], as witnessed by the fact that it is selected as the topic for the live challenge111https://mozart.diei.unipg.it/gdcontest/2025/live/ held in conjunction with GD 2025. Let k be an integer. We say that a graph G is k-planar if it can be drawn in the plane so that each edge involves at most k crossings. The minimum integer k such that G admits a k-planar drawing is called the local crossing number of G, denoted by lcr(G). The problem of deciding if lcr(G)k for a given graph G is called k-Planarity Testing. Unlike Crossing Number, it is already NP-complete to decide whether lcr(G)1 [2, 4, 17, 26]. More strongly, this problem is NP-complete even when the input is restricted to planar graphs with an extra edge [4] or graphs with constant bandwidth [2]. Urschel and Wellens [33] later extended the NP-hardness by showing that k-Planarity Testing is NP-complete for any fixed k1, even if the input is restricted to graphs with local crossing number at most k or at least 2k. This implies that, unless P = NP, there is no polynomial-time (2ε)-approximation for local crossing number for any ε>0.

To overcome such an intractability, Bannister, Cabello, and Eppstein [2] pursued the parameterized complexity of the case of k=1, namely 1-Planarity Testing, by focusing on structural parameterizations. They showed that 1-Planarity Testing is FPT parameterized by treedepth and by feedback edge number (see Section 2 for definitions). As mentioned above, 1-Planarity Testing is NP-complete even on the classes of graphs with bounded bandwidth. This intractability is indeed inherited by wider classes of graphs, such as those with bounded cliquewidth, treewidth, and pathwidth. In this direction, Münch, Pfister, and Rutter [28] recently considered k-Planarity Testing on special cases of pathwidth-bounded graphs and gave exact and approximation algorithms.

Table 1: The table summarizes our and known results. The columns “unbounded”, “parameter”, and “k=1” indicate the results when k is taken as input, as parameter, and k=1, respectively.
(a)
(b)
Figure 1: A visualization of Table 1 of the cases where k is (a) unbounded and (b) parameter. No borderline, dotted borderline, normal borderline, bold borderline mean that the complexity parameterized by the parameter is FPT, unknown, W[1]-hard, paraNP-complete, respectively. The complexities for parameters with () are already known results and the others are our results. An arrow indicates that the upper parameter is bounded from above by a function of the lower parameter. See the footnote2 for the abbreviations of parameter names.

In this paper, we extend results of [2] by considering the general case k1 and show a fine parameterized complexity landscape with respect to graph width parameters (see Table 1 and Figure 1 for a summary of our results222In Figure 1, the names are abbreviated as follows: cw is clique width, diam is diameter, tw is treewidth, lip is longest induced path, dn is domination number, fvs is feedback vertex set, pw is pathwidth, sd is shrub-depth, mw is modular-width, fes is feedback edge set, bw is bandwidth, dpf is distance to path forest, td is treedepth, cvn is cluster vertex deletion number, nd is neighborhood diversity, mln is max leaf number, vi is vertex integrity, tcn is twin cover number, and vc is vertex cover number.). We extend algorithms of [2] by showing that k-Planarity Testing is FPT parameterized by feedback edge set number (Theorem 14) and by treedepth plus k (Theorem 16). More generally, we show that k-Planarity Testing is FPT on Pt-free graphs when parameterized by t+k (Corollary 18). We also give polynomial kernelizations for k-Planarity Testing with respect to vertex cover number (Theorem 23) and neighborhood diversity (Corollary 25). On the negative side, we show that k-Planarity Testing is W[1]-hard when parameterized solely by treedepth (Theorem 6), which indicates that Theorem 16 is tight in a certain sense, and by twin cover number (Theorem 13). We also show that 1-Planarity Testing is W[1]-hard parameterized by distance to path forest (Theorem 9). Similar to [4], the last result also proves that 1-Planarity Testing remains NP-complete even on planar graphs with a single additional edge. Our reduction shows that 1-Planarity Testing is NP-complete even when further adding restrictions that the input graph has feedback vertex set number at most 3. Using a completely different reduction, we show that 1-Planarity Testing is NP-complete even if the input is restricted to graphs with feedback vertex set number 2 (Theorem 5). This reduction also shows that there is no constant factor approximation for k-Planarity Testing unless P = NP, which significantly strengthens the (2ε)-inapproximability result of Urschel and Wellens [33].

Several proofs (marked with ) are omitted and available in the full version.

2 Preliminaries

Let G be a graph. The vertex set and edge set of G are denoted by V(G) and E(G), respectively. For a vertex set XV(G) (resp. edge set XE(G)), GX denotes the graph obtained from G by deleting all elements in X. The subgraph of G induced by XV(G) is denoted by G[X]. For vV(G), NG(v) denotes the set of neighbors of v.

A (topological) drawing Γ of G is a representation in the plane that maps the vertices of G to distinct points in the plane and each edge of G to a non-self-intersecting (Jordan) curve connecting the points corresponding to the endpoints. In the rest of this paper, we may simply refer to these points as vertices and these curves as edges when no confusion is possible. A crossing in Γ is an intersection of distinct edges that is not a common endpoint. We assume that, in any drawing, an edge does not pass through any vertex other than its endpoints, and no three (or more) edges cross at a common point. The crossing number of G, denoted by cr(G), is the minimum number of crossings over all possible drawings of G. For an integer k, a drawing Γ is said to be k-planar if each edge is crossed at most k times. A graph is said to be k-planar if it admits a k-planar drawing. The local crossing number of G, denoted by lcr(G), is the minimum integer k such that G is k-planar. Throughout this paper, we use the following property of local crossing number.

Lemma 1 ().

Let G be a graph and k be a positive integer. Let Gk be the graph obtained from G by subdividing each edge k1 times. Then, lcr(G)k if and only if lcr(Gk)1.

In this paper, we present parameterized algorithms and hardness results with respect to several graph width parameters. However, some of them may not need to be defined precisely. Hence, we only provide the definitions that are directly needed to present our results. For basic concepts in parameterized complexity, we refer the reader to [5].

Let G be a graph. A vertex cover of G is a set of vertices S such that GS is edge-less, and the vertex cover number of G is the minimum size of a vertex cover of G, which is denoted by vc(G). The feedback vertex set number (resp. feedback edge set number) of G, denoted by fvs(G) (resp. fes(G)), is the minimum cardinality of a vertex set (resp. edge set) X such that GX is acyclic. An elimination forest of G is a rooted forest F (i.e., a set of rooted trees) such that V(F)=V(G) and for every edge in G, one of the endpoints is an ancestor of the other in F. The treedepth of G, denoted by td(G), is the minimum integer k such that G has an elimination forest of height k. Two vertices u and v are called true twins if they are adjacent and NG(u)=NG(v); they are called false twins if they are non-adjacent and NG(u)=NG(v). The neighborhood diversity of G is at most k if the vertex set of G can be partitioned into k sets V1,,Vk such that either all the pairs in Vi are true twins or they are false twins. Each set Vi is called a twin class. Note that each true twin class induces a clique and each false twin class induces an independent set in G. The neighborhood diversity of G is denoted by nd(G). A vertex set SV(G) is called a twin cover of G if each connected component of GS consists of true twins in G. The twin cover number of G is the minimum size of a twin cover of G. The max leaf number of G is the maximum number of leaves of a spanning tree of G.

3 Hardness

In this section, we give several intractability results for k-Planarity Testing and 1-Planarity Testing. In particular, we show that k-Planarity Testing is hard to approximate within any constant factor in polynomial time, even on graphs with feedback vertex set number at most 2, and 1-Planarity Testing is NP-complete even on near-planar graphs with feedback vertex set number 3, where a graph is near-planar if it can be obtained from a planar graph by adding a single edge. These two results improve the previous results of [33] and [4]. To this end, we design two completely different reductions, one is given from Two-Sided k-Planarity and the other is given from Unary Bin Packing, which also yield several consequences other than these two results.

3.1 Inapproximability for Graphs with Feedback Vertex Set Number 2

In this subsection, we show that it is NP-hard to approximate the local crossing number of a given graph within any constant factor, even if the graph has feedback vertex set number 2.

Before describing our reductions, we start with a technical lemma, showing that by adding some vertices and edges to a graph, one can impose a certain form on its k-planar drawings.

Let G be a graph with m edges and let k be a positive integer. Let XV(G) be a nonempty vertex subset. We define a new graph G by adding a vertex r and km+1 paths of length 2 between r and v for each vX to G.333The length of a path is defined as the number of edges in it. We refer to these paths of length 2 as spokes.

Lemma 2 ().

Suppose that G has a k-planar drawing Γ. Then, there is a k-planar drawing Γ of G such that each spoke has no crossings and the subdrawings of Γ and Γ induced by G are identical.

Proof (sketch).

Since the edges in G can cross other edges km times in total, for each uX, there is a spoke between r and u that does not cross any edges of G. Let Pu be such a spoke for each uX and 𝒫{PuuX}. Although spokes in 𝒫 may cross, we can untangle them, preserving the subdrawing for G. Then we redraw the other spokes along 𝒫.

We will use this lemma in the following form.

Corollary 3.

Let SV(G). Let G be a graph obtained from G by adding a vertex s and k|E(G)|+1 spokes between s and each uS. Moreover, let SV(G) and let G′′ be a graph obtained from G by adding a vertex s and k|E(G)|+1 spokes between s and each uS. Suppose that G′′ has a k-planar drawing. Then, there is a k-planar drawing of G′′ such that all spokes have no crossings.

Proof.

By Lemma 2, G′′ has a k-planar drawing Γ in which all the spokes incident to s have no crossings. Let Γ be the subdrawing of Γ induced by G. As shown in the proof of Lemma 2, for each uS, there is a spoke Pu between s and u that has no crossing with any edge of G in Γ. Since this spoke Pu does not cross any spoke incident to s, the uncrossing operation and redrawing spokes between s and u used in Lemma 2 never create a new crossing with spokes incident to s. Thus, by applying Lemma 2 to G and Γ, we have a k-planar drawing of G′′ being claimed.

We now turn to our reduction from Two-Sided k-Planarity, in which we are given a bipartite graph G=(XY,E) with two independent sets X and Y, and an integer k. The goal is to determine whether G has a 2-layer k-planar drawing, which is a special case of a k-planar drawing in which the vertices in X are drawn on a line, the vertices in Y are drawn on another line parallel to the first line, and each edge is drawn as a straight line.

The idea of our reduction is the same as that of Garey and Johnson [15] from Bipartite Crossing Number to Crossing Number. We remark that, however, the proof is more involved due to the difficulty of k-planarity.

Let G=(X,Y,E),k be an instance of Two-Sided k-Planarity, where nX=|X|, nY=|Y|, and m=|E|. We construct a graph G from G as follows (see Figure 2):

  1. 1.

    we introduce two vertices uX,uY;

  2. 2.

    for each xX, add 1km+1 spokes between uX and x;

  3. 3.

    for each yY{uX}, add 2k(1nX+m)+1 spokes between uY and y.

Figure 2: An illustration of the graph G constructed from an instance G,k of Two-Sided k-Planarity. Note that the numbers of spokes are different from that of the actual construction.

Then, G,k is the instance we construct for k-Planarity Testing. It is easy to verify that the above construction can be done in polynomial time.

Lemma 4.

The graph G admits a 2-layer k-planar drawing if and only if the graph G admits a k-planar drawing.

Proof.

It is clear that we can obtain a k-planar drawing of G from a 2-layer k-planar drawing of G by drawing uX, uY, and spokes as Figure 2. Hence, we only show the other direction.

Suppose that the graph G admits a k-planar drawing Γ. By Corollary 3, we can assume that all the spokes in G have no crossings in Γ. We then replace the set of spokes connecting two vertices with a single edge between them and contract the edge between uX and uY by identifying them into a new vertex u in Γ. We let G denote the graph obtained in this way. As all spokes have no crossings, the obtained drawing of G, denoted by Γ, is still k-planar. Let us note that G is isomorphic to the graph obtained from G by adding a universal vertex u (which is a vertex adjacent to all the vertices in G). We claim that we can draw a closed curve C passing through all the vertices in V(G){u} such that

  1. 1.

    no edge of G intersects C (i.e., C is a noose in Γ);

  2. 2.

    all the edges incident to u are contained in one of the two regions R with boundary C;

  3. 3.

    all the edges in E(G) are contained in the other region with boundary C;

  4. 4.

    all the vertices in X appear consecutively on C;

  5. 5.

    all the vertices in Y appear consecutively on C.

To this end, we draw a curve C while keeping track of the cyclic ordering of the edges incident to u (and hence the neighbors of u) in Γ. As these incident edges have no crossings, we can draw such a curve satisfying (1), (2), and (3). To see the properties (4) and (5), suppose for contradiction that there are x,xX and y,yY such that x,y,x,y appear in this order on C. Due to the property (2), C can be seen as a closed curve on Γ such that the region corresponding to R contains all spokes of G. Let Px be a path in G between x and x via uX consisting only of spokes. We define Py similarly. In Γ those paths are contained in R and C. However, since the vertices x,x,y,y are all placed on C, such a drawing must yield a crossing, which contradicts the fact that spokes in G have no crossings in Γ.

Let X={x1,,xnX} and let Y={y1,,ynY} such that x1,,xnX,y1,,ynY appear on C in this order. We now convert the drawing Γ into a drawing Γ^ such that

  • all the vertices in V(G){u} are placed on a line in the order x1,,xnX,y1,,ynY;

  • all the edges incident to u are drawn as straight segments in a half plane separated by ;

  • all the other edges are drawn in the other half plane in such a way that the curve corresponding to an edge forms a semicircular arc between the endpoints.

See Figure 3(a) for an illustration.

(a)
(b)
Figure 3: Two topologically equivalent drawings of G, where the subdrawing induced by G is (a) an arc diagram, and (b) a 2-layer drawing.

Observe that this drawing Γ^ is also k-planar as two edges of G cross in Γ^ only if they cross in Γ as well. Moreover, Γ^ is simple, meaning that no pair of edges crosses more than once and no two edges incident to a common vertex cross. This in turn can be converted into a k-planar drawing Γ as in Figure 3(b). The subdrawing of Γ induced by G{u} is a 2-layer k-planar drawing such that two edges in G{u} cross if and only if they cross in Γ^. Hence, G (=G{u}) admits a 2-layer k-planar drawing.

This reduction immediately gives us the following consequence. Note that Theorem 5 is optimal in the sense that every graph with feedback vertex set number 1 is planar.

Theorem 5.

1-Planarity Testing is NP-complete even if the given graph has feedback vertex set number 2.

Proof.

The NP-membership of 1-Planarity Testing is shown in [26]. It is known that Two-Sided k-Planarity is NP-complete even on trees [25, Theorem 11]. Thus, the graph G constructed in the proof of Lemma 4 has feedback vertex set number at most 2. This in turn implies the claim by Lemma 1.

The above reduction leads to further consequences. To see this, we first sketch the reduction used in [25, Theorem 11]. They performed a polynomial-time reduction from Bandwidth to Two-Sided k-Planarity. Let T be a tree, b be a positive integer, and be an arbitrary even number with 2b2. For each edge in T, each edge eE(T) is subdivided once by introducing a new vertex we, and we add leaves adjacent to each original vertex in T. They showed that there is an ordering v1,,v|V(T)| of V(T) such that |ij|b for any edge {vi,vj}E(T) if and only if the obtained graph T has a 2-layer k-planar drawing with k=(+4)(b1)/2.

Theorem 6.

k-Planarity Testing is W[1]-hard with respect to treedepth, even on graphs with feedback vertex set number 2.

Proof.

It is known that the problem Bandwidth is W[1]-hard with respect to treedepth, even on trees [16]. The above reduction we sketched [25, Theorem 11] also preserves the boundedness of treedepth. To see this, we convert an elimination forest F of T to that of T as follows. Starting from F, we add the leaves attached to each vertex as leaf children of it. For each edge eE(T), one of the end vertices, say v, is a descendant of the other in F. We then add we as a leaf child of v in F. It is easy to verify that the rooted forest obtained in this way is an elimination forest of T and its height increases by at most 1 compared to the original elimination forest of T, yielding that td(T)td(T)+1. Hence, Two-Sided k-Planarity is also W[1]-hard with respect to treedepth, even on trees. Moreover, the reduction used in Lemma 4 increases the treedepth by at most 3, which implies the claim.

Lemma 7.

Unless P = NP, for any constant c1, there is no polynomial-time c-approximation algorithm for Two-Sided k-Planarity on trees.

Proof.

For any constant c1, it is NP-hard to distinguish the following cases: the bandwidth of T is at most b or more than cb for a given tree T and an integer b [10]. We set =2t4 for sufficiently large t and construct a tree T according to the above reduction from T. If the bandwidth of T is at most b, then we have lcr(T)t(b1). Otherwise, we have lcr(T)tcb. This implies that a polynomial-time c-approximation algorithm for Two-Sided k-Planarity on trees distinguishes the above two cases.

Combining Lemma 4 and Lemma 7, we obtain the following.

Theorem 8.

Unless P = NP, for any constant c1, there is no polynomial-time c-approximation algorithm for k-Planarity Testing, even if the given graph has feedback vertex set number 2.

3.2 Distance to Path Forest

In this subsection, we show that 1-Planarity Testing is W[1]-hard parameterized by distance to path forest, i.e., the minimum number of vertices required to make the graph into a disjoint union of paths. The underlying idea of the proof is similar to the one in [17]. We perform a reduction from Unary Bin Packing. An instance I of Unary Bin Packing consists of a (multi)set of positive integers S and positive integers B and b such that xSx=bB and B=O(poly(|S|)). Then the goal is to determine if it is possible to partition S into b sets so that the sum of each set is exactly B. Unary Bin Packing is NP-complete when b is given as input [15] and W[1]-hard when parameterized by b [22].

Theorem 9.

1-Planarity Testing is W[1]-hard with respect to distance to path forest.

Proof.

Let S={x1,,x|S|},B,b be an instance of Unary Bin Packing. In the following, we assume that b3. We construct an instance G=(V,E) of 1-Planarity Testing as follows. Let G be a graph consisting of vertices u1,u2,v1,,vb, and paths P1,,P|S| with |V(Pi)|=xi for 1i|S|. Let 𝒫={P1,,P|S|}. We add a path of length B between vi and vi+1 for each 1ib, where vb+1v1. These paths form a cycle C of bB vertices, passing through vi for all i. Next, we add an edge between ui and each vertex in Pj for 1i2 and 1j|S|. Let m be the number of edges in the graph constructed so far (namely, m=4bB|S|). Finally, for 1ib, we add 1m+1 spokes between u1 and vi and add 2(1b+m)+1 spokes between u2 and vi. See Figure 4 for an illustration of the constructed graph G. Observe that, by construction, removing b+2 vertices, say u1,u2,v1,,vb, yields a path forest. The construction can be done in polynomial time.

Figure 4: An illustration of the reduction for Theorem 9. Note that the number of depicted spokes between ui and vj is not accurate for aesthetic purposes.

From a solution of Unary Bin Packing, we can obtain a 1-planar drawing of G easily, as in Figure 5.

Figure 5: A 1-planar drawing of the graph constructed from an instance S={1,2,2,3,4},B=4,b=3 of Unary Bin Packing. Each thick edge represents the spokes between its endpoints.

To be more precise, let {S1,,Sb} be a solution for Unary Bin Packing. We first draw the cycle C as a circle in such a way that the spokes between u1 and vi partition the interior of the circle into b regions R1,,Rb, where Ri is enclosed by a spoke between u1 and vi, a spoke between u1 and vi+1, and a path between vi and vi+1 on C. For each region Ri, we draw a path Pj𝒫 inside Ri if xjSi. Since the sum of the numbers of vertices of the paths drawn in Rj is exactly B, we can draw those paths so that each edge in the path between vi and vi+1 is crossed exactly once. Hence, we can draw G so that each edge is crossed at most once.

For the other direction, suppose that G has a 1-planar drawing. By Corollary 3, there exists a 1-planar drawing Γ of the graph G such that all the spokes in G have no crossings. Since the spokes have no crossings in Γ, the subdrawing induced by them is planar, and hence there are two vertices vi and vj that belong to the outer cycle of this subdrawing. As b3, all the other v’s are drawn inside the region bounded by this outer cycle. Moreover, since two consecutive v and v+1 are connected by a path in C, the two distinguished vertices vi and vj must be consecutive, namely j=(i+1)modb. By appropriately renaming those vertices, we can assume that G1i|S|V(Pi) are drawn as in Figure 6(a). Then, there are b internally vertex-disjoint paths Q1,,Qb between u1 and u2 such that Qi is a concatenation of spokes between u1 and vi and between vi and u2 for each i. These paths separate the plane into b regions, R1,,Rb as Figure 6(b).

(a)
(b)
Figure 6: The subgraphs induced by (a) cycle C and spokes (boldlines) and (b) the paths Q1,,Qb, which defines b regions R1,,Rb.

As each path Pi cannot lie in two different regions, 𝒫 can be partitioned into {𝒫1,,𝒫b}, where 𝒫i𝒫 is the collection of paths drawn inside Ri. Observe that, for each path Pj𝒫i, there are xi=|V(Pi)| edge-disjoint paths between u1 and u2 that are drawn inside Ri. Each of them must cross the path between vi and vi+1 on C, implying that there are at most B such paths inside Ri as Γ is 1-planar. Since there are exactly b regions, each 𝒫i must satisfy P𝒫i|V(P)|=B. Therefore, we can partition S into b sets such that the sum of each set is exactly B.

Observe that the above reduction still works even if we add an edge between u1 and each vertex in C, in which case the vertex set {u1,u2} forms a dominating set.

Corollary 10.

1-Planarity Testing is NP-complete even if the domination number is 2.

As mentioned in Section 1, Cabello and Mohar [4] showed that 1-Planarity Testing is NP-hard even on near-planar graphs. Theorem 9 strengthens their hardness result as the graph G constructed in the reduction is a near-planar graph with the following properties.

Corollary 11.

1-Planarity Testing is NP-complete on near-planar graphs with feedback vertex set number at most 3 and pathwidth at most 4.

3.3 Twin Cover Number

In this subsection, we show that k-Planarity Testing is W[1]-hard with respect to twin cover number, which complements the positive result of the “+k setting” to be presented in the subsequent section.

We give a similar reduction from Unary Bin Packing as in Section 3.2. However, in order to substitute cliques for paths, we first show the W[1]-hardness of a restricted version of Unary Bin Packing, where integers in S are sufficiently small that the constructed graph admits a B-planar drawing.

Lemma 12 ().

Unary Bin Packing is W[1]-hard parameterized by the number b of bins, even if each integer xS satisfies xB1.

Proof (sketch).

We add 2bB copies of B+1 to S of an instance of Unary Bin Packing and increase the capacity of a bin by 2B(B+1) to obtain an instance with the claimed condition. We can show that there is no other solution than to distribute those B+1’s evenly to b bins, making it equivalent to the original instance.

Theorem 13.

k-Planarity Testing is W[1]-hard parameterized by twin cover number.

Proof.

The reduction is almost analogous to the one used in Theorem 9. Let I=S,B,b be an instance of Unary Bin Packing. We can assume that b3 and xB1 for all xS due to Lemma 12. While the basic construction of the graph G is the same as in Theorem 9, it differs in the following points:

  • the cycle C contains exactly b vertices v1,,vb;

  • for each xiS, G contains a clique Ci with xi vertices, instead of a path Pi, such that each vertex in the clique is adjacent to both u1 and u2;

  • for each vi, we add 1Bm+1 spokes between u1 and vi and add 2B(b1+m)+1 spokes between u2 and v2, where m is the number of edges in G that are not contained in the spokes.

When we draw G as in Figure 5, edges incident to a vertex in clique Ci may have crossings. Each edge e in Ci may cross other edges in Ci and all the edges between uj and a vertex in Ci for 1j2. This implies that the number of crossings that e involves is at most

|E(Ci)|1+2|V(Ci)|(B1)(B2)/21+2(B1)<B.

Similarly, we can conclude that each edge between uj and a vertex in Ci has at most B1 crossings. Since we can assume that all the spokes in G have no crossings due to Corollary 3, these spokes define exactly b regions, each of which contains exactly B vertices of cliques. Thus, we can show that I is a yes-instance if and only if G is B-planar as well.

As each connected component in G(V(C){u1,u2}) consists of true twins, G has a twin cover of size at most b+2. This completes the proof.

4 FPT Algorithms

In contrast to the previous section, we mainly focus on the algorithmic aspects of k-Planarity Testing in this section and show that k-Planarity Testing is FPT when several well-known graph parameters (and k) are given as parameters.

4.1 Reducing to 𝟏-Planarity Testing

There are several consequences from Lemma 1, combining known FPT algorithms of 1-Planarity Testing of [2].

Let G be a graph and let k be an integer. Let Gk be the graph obtained from G by subdividing each edge k1 times. Clearly, we have fes(G)=fes(Gk). By Lemma 1, G is k-planar if and only if Gk is 1-planar. Since 1-Planarity Testing is FPT parameterized by feedback edge set number [2], the following theorem holds.

Theorem 14.

k-Planarity Testing is FPT parameterized by feedback edge set number.

We next observe that the treedepth of Gk is not much larger than the treedepth of G.

Lemma 15 ().

For an integer k0, it holds that td(Gk)td(G)+log2k.

Similarly to the above, we have the following theorem.

Theorem 16.

k-Planarity Testing is FPT parameterized by treedepth+k.

We would like to note that a similar result is already mentioned in [34]. Since k-planarity is closed under vertex and edge deletions, it is known that the class of k-planar graphs with bounded treedepth is well-quasi-ordered by the induced-subgraph relation [30]. This implies a non-uniform version of Theorem 16: k-Planarity Testing can be solved by just checking whether G has an induced subgraph that belongs to a finite set k,td of graphs, where the size of k,td depends only on k and td(G).

Theorem 16 leads to FPT results for other parameters. To explain this, we introduce the notion of degeneracy. For an integer d0, a graph G is said to be d-degenerate if each nonempty subgraph of G contains a vertex of degree at most d. A graph class 𝒞 is degenerate if there exists d such that every graph in 𝒞 is d-degenerate.

Proposition 17 ([29, Proposition 6.4]).

A hereditary class of graphs 𝒞 has bounded treedepth if and only if 𝒞 is degenerate and does not contain a path Pt for some t as an induced subgraph.

It is known that each k-planar graph with n vertices has at most 3.81kn edges [1]. Since k-planarity is closed under taking a subgraph, the class of k-planar graphs is degenerate for every fixed k. Hence, combining with Proposition 17, k-Planarity Testing on Pt-free graphs parameterized by k+t can be reduced to the one parameterized by treedepth+k. It is known that a graph class 𝒞 does not contain a path Pt for some t as an induced subgraph if 𝒞 has bounded shrub-depth [14] or bounded modular-width [24].

Corollary 18.

Let 𝒞 be a hereditary class of graphs that excludes some path Pt as an induced subgraph. Then, k-Planarity Testing over 𝒞 is FPT parameterized by k+t. In particular, k-Planarity Testing is FPT parameterized by shrub-depth+k, and modular-width+k.

4.2 Polynomial Kernels

In this subsection, we describe a kernelization algorithm for k-Planarity Testing parameterized by vertex cover number+k. The high-level ideas follow the polynomial kernel for 1-Planarity Testing due to Bannister, Cabello, and Eppstein [2].

Let G be a graph and let S be a vertex cover of G. We can find such a vertex cover S of size at most twice the optimum in linear time. First, we observe that the “diversity” of vertices outside of a vertex cover S is sufficiently small when G is k-planar. For a function f, let #f(X) denote |{f(x)xX}|, the number of possible images of X through f.

Lemma 19.

Let I2V(G)S be the set of vertices with degree at least 2 in V(G)S. Let π be a function that maps each vertex vI2 to an unordered pair of its distinct neighbors (that is, π:I2(NG(v)2)). If #π(I2)>|S|3.812k, then G is not k-planar.

Proof.

Let GS be a graph with V(GS)=S and E(GS)=π(I2). Let G be the graph obtained from GS by subdividing each edge once. Then G is a subgraph of G, and hence G is k-planar. This implies that by Lemma 1, GS is 2k-planar. Hence, #π(I2)=|E(GS)||S|3.812k, where the last inequality is shown in [1].

It is known that, for a positive integer k, if G contains K7k+1,3 as a subgraph then G is not k-planar [6, 32]. Hence, the following also immediately holds.

Corollary 20.

Let uV(G)S be a vertex of degree at least 3. If there are at least 7k+1 false twins of u (including u itself), then the graph G is not k-planar.

Next, we consider vertices of degree 2. The following lemma is a key ingredient of our kernelization. It is worth mentioning that, although a similar reduction rule is used in the case k=1 [2], the proof for the general case k1 is considerably more involved.

Lemma 21.

Let uV(G)S be a vertex of degree 2 with NG(u)={s1,s2}S. If there are more than 16k2|S| vertices in V(G)S with the same neighborhood {s1,s2}, then G is k-planar if and only if G{u} is k-planar.

Proof.

The () direction is clear. Suppose that GG{u} is k-planar and let Γ be a k-planar drawing of G. We can assume that G has no vertex of degree 1.

Let Tu={vV(G)SNG(v)={s1,s2}}. Then, we have tu=|Tu|16k2|S|. Let 𝒫 be the set of paths between s1 and s2 via a vertex in Tu. Let us align the paths in 𝒫 according to the cyclic order in Γ around s1, as P0,P1,,Ptu1. For two paths Pi,Pj, the cyclic difference of Pi and Pj is defined as min{ji,ij+tu}. We then claim the following.

Claim 22.

Two paths Pi,Pj𝒫 with the cyclic difference at least 4k do not cross in Γ.

Proof.

Figure 7: The Jordan curve C, the crossing point c, and the vertex s1 with edges going out to each of the interior and exterior of C due to the cyclic difference condition.

Suppose that Pi and Pj do cross in Γ. Let c be a crossing point between Pi and Pj, and let C be a Jordan curve consisting of two arcs: the arc of Pi from s1 to c and the arc of Pj from c to s1. Such a crossing point c can be chosen so that C forms a closed curve. In both cases where s2 is in the interior or in the exterior of regions bounded by C, from the condition that the cyclic difference of Pi and Pj is at least 4k, there are at least 4k1 edges that must cross C as shown in Figure 7. As Pi,Pj can have at most 2k1 crossings other than c each, and hence 4k2 crossings in total, this leads to a contradiction.

Figure 8: Let P be a path whose internal vertices have degree exactly 2. A “self-crossing” on the path can be removed by suppressing the “loop” formed by the edges in P.

Let 𝒫={P4ki0i<4k|S|}. As the cyclic difference of any pair of two paths in 𝒫 is at least 4k, by Claim 22, no two paths in 𝒫 cross. We can also assume that each path in 𝒫 does not cross itself, as it can be resolved as shown in Figure 8. Hence, they separate the plane into 4k|S| regions similarly to Figure 6(b). For each 0i4k|S|, let Ri be the region that is bounded by two paths P4ki and P4k(i+1), where P4k4k|S|P0. Observe that, for each vertex vS{s1,s2}, the number of paths in 𝒫 crossed by an edge incident to NG(v) is at most 4k: when v belongs to region Ri, each edge incident to a vertex in NG(v) can only be in regions at most 2k apart from Ri, namely, {Rjmod4k|S|i2kji+2k}. Since S is a vertex cover of G, for every edge eE(G), e={s1,s2} (if it exists); e is contained in a path in 𝒫; or e is incident to some vertex in S{s1,s2} due to the assumption that there is no degree-1 vertex. Since at most k paths in 𝒫 can be crossed by e={s1,s2} (if it exists) and at most 4k(|S|2) paths in 𝒫 can be crossed by an edge incident to a vertex in S{s1,s2}, there are at least 4k|S|(4k(|S|2))k=7k paths in 𝒫 that are crossed only by the paths in 𝒫 or not crossed at all.

Let P𝒫 be such a path. We remove all the paths in 𝒫 but P from Γ. Observe that now P has no crossings at all. Hence, we can redraw all the removed paths along P without making a crossing. We can also add the path (s1,u,s2) to Γ along P in the same way and obtain a k-planar drawing of G.

By summarizing the above lemmas, we can obtain a kernelization algorithm.

Theorem 23 ().

k-Planarity Testing has a kernel with O(vc(G)2k2k) vertices. Moreover, such a kernel can be computed in linear time.

We can obtain a similar result for the case of neighborhood diversity.

Lemma 24 ().

The vertex cover number of a k-planar graph G is at most O(nd(G)k).

By plugging Lemma 24 to Theorem 23, we have the following.

Corollary 25.

k-Planarity Testing has a kernel with O(nd(G)2k3k) vertices.

Finally, we remark that the results of Theorem 23 and Corollary 25 cannot be generalized to vertex integrity and modular-width parameterizations (see [13, 16] for their definitions) unless NPcoNP/poly, respectively. Let G1,,Gt be graphs and G be the disjoint union of G1,,Gt. Let vi(G) be the vertex integrity of G and let mw(G) be the modular-width of G. Then, it holds that (1) G is 1-planar if and only if Gi is 1-planar for all 1it and (2) vi(G)maxi|V(Gi)| and mw(G)maxi|V(Gi)|. This implies that 1-Planarity Testing parameterized by vertex integrity and by modular-width is AND-cross-compositional [3, 9, 12], implying that unless NPcoNP/poly, there is no polynomial kernelization for 1-Planarity Testing of size poly(vi(G)) or poly(mw(G)). As td(G)vi(G), this kernelization lower bound also holds for treedepth parameterization.

5 Conclusion

In this paper, we study the parameterized complexity of k-Planarity Testing from the perspective of graph structural parameterizations and show several (tight) upper and lower bound results. We leave several interesting open problems relevant to our results.

When k is considered as input, we have only shown that k-Planarity Testing is FPT parameterized by feedback edge set number. It would be interesting to seek similar results using other graph parameters. In this direction, a highly related problem Crossing Number is known to be FPT parameterized by vertex cover number [19]. However, it seems that their approach cannot be directly applied to k-Planarity Testing, requiring new insights for this problem. Towards this, showing intractability with a general parameter of vertex cover number, such as vertex integrity or neighborhood diversity (see Figure 1(a)), would also be a nice open problem.

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