Fast Diameter Computation within Split Graphs

10/08/2019 ∙ by Guillaume Ducoffe, et al. ∙ 0

When can we compute the diameter of a graph in quasi linear time? We address this question for the class of split graphs, that we observe to be the hardest instances for deciding whether the diameter is at most two. We stress that although the diameter of a non-complete split graph can only be either 2 or 3, under the Strong Exponential-Time Hypothesis (SETH) we cannot compute the diameter of a split graph in less than quadratic time. Therefore it is worth to study the complexity of diameter computation on subclasses of split graphs, in order to better understand the complexity border. Specifically, we consider the split graphs with bounded clique-interval number and their complements, with the former being a natural variation of the concept of interval number for split graphs that we introduce in this paper. We first discuss the relations between the clique-interval number and other graph invariants such as the classic interval number of graphs, the treewidth, the VC-dimension and the stabbing number of a related hypergraph. Then, in part based on these above relations, we almost completely settle the complexity of diameter computation on these subclasses of split graphs: ∙ For the k-clique-interval split graphs, we can compute their diameter in truly subquadratic time if k = O(1), and even in quasi linear time if k = o(log n) and in addition a corresponding ordering is given. However, under SETH this cannot be done in truly subquadratic time for any k = ω(log n). ∙ For the complements of k-clique-interval split graphs, we can compute their diameter in truly subquadratic time if k = O(1), and even in time O(km) if a corresponding ordering is given. Again this latter result is optimal under SETH up to polylogarithmic factors. Our findings raise the question whether a k-clique interval ordering can always be computed in quasi linear time. We prove that it is the case for k = 1 and for some subclasses such as bounded-treewidth split graphs, threshold graphs and comparability split graphs. Finally, we prove that some important subclasses of split graphs-including the ones mentioned above-have a bounded clique-interval number.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

Computing the diameter of a graph (maximum number of edges on a shortest path) is a fundamental problem with countless applications in computer science and beyond. Unfortunately, the textbook algorithm for computing the diameter of an -vertex -edge graph takes -time. This quadratic running-time is too prohibitive for large graphs with millions of nodes. As already noticed in [15, 16], an algorithm breaking this quadratic barrier for general graphs is unlikely to exist since it would lead to more efficient algorithms for some disjoint set problems and, as proved in [30], the latter would falsify the Strong Exponential-Time Hypothesis (SETH). This raises the question of when we can compute the diameter faster than . By restricting ourselves to more structured graph classes, here we hope in obtaining a finer-grained dichotomy between “easy” and “hard” instances for diameter computations – with the former being quasi linear-time solvable and the latter being impossible to solve in subquadratic time under some complexity assumptions.

Specifically, we focus in this work on the class of split graphs, i.e., the graphs that can be bipartitioned into a clique and a stable set. – For any undefined graph terminology, see [5]. – This is one of the most basic idealized models of core/periphery structure in complex networks [7]. We stress that every split graph has diameter at most three. In particular, computing the diameter of a non-complete split graph boils down to decide whether this is either two or three. Nevertheless, under the Strong Exponential-Time Hypothesis (SETH) the textbook algorithm is optimal even for split graphs [6]. We observe that the split graphs are in some sense the hardest instances for deciding whether the diameter is at most two. For that, let us bipartition a split graph into a maximal clique and a stable set ; it takes linear time [23]. The sparse representation of is defined as 444 This sparse representation is also called the neighbourhood set system of the stable set of , see Sec. 2.. – Note that all our algorithms in this paper run in time linear in the size of this above representation. –

Observation 1

Deciding whether an -vertex graph has diameter two can be reduced in linear time to deciding whether a -vertex split graph (given by its sparse representation) has diameter two.

Proof. If then, let be a disjoint copy of . For every we denote by its copy. We define where . By construction, is a split graph with maximal clique and stable set . Furthermore, if and only if .

We here address the fine-grained complexity of diameter computation on subclasses of split graphs. By the above Observation 1, our results can be applied to the study of the diameter-two problem on general graphs.

Related work.

Exact and approximate distance computations for chordal graphs: a far-reaching generalization of split graphs, have been a common research topic over the last few decades [10, 11, 17, 18]. To the best of our knowledge, this is the first study on the complexity of diameter computation on split graphs. However, there exist linear-time algorithms for computing the diameter on some other subclasses of chordal graphs such as: interval graphs [29], or strongly chordal graphs [9]. These results imply the existence of linear-time algorithms for diameter computations on interval split graphs and strongly chordal split graphs, among other subclasses.

Beyond chordal graphs, the complexity of diameter computation has been considered for many graph classes, e.g., see [16] and the papers cited therein. In particular, the diameter of general graphs with treewidth can be computed in quasi linear time [1], whereas under SETH we cannot compute the diameter of split graphs with clique-number (and so, treewidth) in subquadratic time [6]. We stress that our two first examples of “easy” subclasses, namely: interval split graphs and strongly chordal split graphs have unbounded treewidth. Our work unifies almost all known tractable cases for diameter computation on split graphs – and offers some new such cases – through a new increasing hierarchy of subclasses.

Relatedly, we proved in a companion paper [20] that on the proper minor-closed graph classes and the bounded-diameter graphs of constant distance VC-dimension (not necessarily split) we can compute the diameter in time , for some small . We consider in this work some subclasses of split graphs of constant distance VC-dimension. However, the time bounds obtained in [20] are barely subquadratic. For instance, although the graphs of constant treewidth fit in our framework, our techniques in [20] do not suffice for computing their diameter in quasi linear time. In fact, neither are they sufficient to explain why we can compute the diameter in subquadratic time on graphs of superconstant treewidth . Unlike [20] this article is a new step toward characterizing the graph classes for which we can compute the diameter in quasi linear time.

Our results.

We introduce a new invariant for split graphs, that we call the clique-interval number. Formally, for any , a split graph is -clique-interval if the vertices in the clique can be totally ordered in such a way that the neighbours of any vertex in the stable set form at most intervals. The clique-interval number of a split graph is the minimum such that it is -clique-interval. Although this definition is quite similar to the one of the interval number [28], we show in Sec. 2 that being -clique-interval does not imply being -interval, and vice-versa555 We observe that the general graphs with bounded interval number are sometimes called “split interval” [2], that may create some confusion.. In fact, as we also proved in Sec. 2, the clique-interval number of a split graph is more closely related to the VC-dimension and the stabbing number of the neighbourhood set system of its stable set. Nevertheless, a weak relationship with the interval number can also be derived in this way.

We then study in Sec. 3 what the complexity of computing the diameter is on split graphs parameterized by the clique-interval number. It follows from our results in Sec. 2 and those in [20] that on every subclass of constant clique-interval number, there exists a subquadratic-time algorithm for diameter computation. Our work completes this general result as it provides an almost complete characterization of the quasi linear-time solvable instances. As a warm-up, we observe in Sec. 3.1 that on clique-interval split graphs (a.k.a., -clique-interval), deciding whether the diameter is two is equivalent to testing for a universal vertex. We give a direct proof of this result and another one based on the inclusion of clique-interval split graphs in the subclass of strongly chordal split graphs. On the way, we prove more generally – and perhaps surprisingly – that for the intersection of split graphs with many interesting graph classes from the literature, having diameter at most two is equivalent to having a universal vertex! Then, we address the more general case of -clique-interval split graphs, for .

  • Our first main contribution is the following almost dichotomy result (Theorem 2). For every -vertex -clique-interval split graph, we can compute its diameter in quasi linear-time if and a corresponding total ordering of its clique is given. This result follows from an all new application of a generic framework based on -range trees [3], that was already used for diameter computations on some special cases [1, 19] but with a quite different approach than ours. Furthermore, the logarithmic upper bound on is somewhat tight. Indeed, we also prove that under SETH we cannot compute in subquadratic time the diameter of -clique-interval split graphs for .

    We note that this above result is quite similar to the one obtained in [1] for treewidth. Indeed, we observe that every split graph of treewidth is -clique-interval (and even -clique-interval, see Sec. 2). We use this easy observation so as to prove our conditional time complexity lower-bound.

  • Then, we focus on the complements of -clique-interval split graphs — that are an interesting subclass in their own right since they generalize, e.g., interval split graphs. For the latter we get a more straightforward algorithm for diameter computation, with a better dependency in . Indeed, we prove that we can compute the diameter of such graphs in time if a corresponding ordering is given. This result is conditionally optimal up to polylogarithmic factors because and, under SETH, we cannot compute the diameter of split graphs with edges in subquadratic time [1].

It follows from these two above results that having at hands a -clique-interval ordering for a split graph or its complement can help to significantly improve the time complexity for computing its diameter. We so ask whether such orderings can be computed in quasi linear time, for some small values of . In Sec. 4 we prove that it is indeed the case for bounded-treewidth split graphs (trivially) and some other dense subclasses of bounded clique-interval number such as comparability split graphs. Finally our main result in this section is that the clique-interval split graphs can be recognized in linear time.

Overall, we believe that our study of -clique-interval split graphs is a promising framework in order to prove, somewhat automatically, new quasi linear-time solvable special cases for diameter computations on split graphs and beyond.

2 Clique-interval numbers and other graph parameters

We start by relating the clique-interval number of split graphs with better-studied invariants from Graph theory and Computational geometry.

Treewidth.

First we observe that if is a split graph then, for any total order over and any , is the union of at most intervals. In fact we can improve this rough upper-bound, as follows:

Lemma 1

Every split graph with clique-number (and so, treewidth) at most is -clique-interval.

Proof. Let be a split graph with clique-number . Let be any total ordering of . For any integer , and any , we claim that is the union of at most intervals of . Indeed if has at least non-neighbours, then it has at most neighbours and so the claim trivially holds. Otherwise, has less than non-neighbours in , but then the other nodes form at most intervals of . This bound is optimal for .

Conversely, since any complete graph is -clique-interval, the clique-interval number cannot be bounded by any function of the treewidth.

VC-dimension and Stabbing number.

For a set system (a.k.a., range space or hypergraph), we say that a subset is shattered if is the power-set of . The VC-dimension of a finite set system is the largest size of a shattered subset. We now prove an intriguing connection between the clique-interval number of a split graph and the VC-dimension of a related set system.

Proposition 1

For any split graph , let . If is -clique-interval then has VC-dimension at most .

Proof. Suppose by contradiction that is -clique-interval but has VC-dimension at least . Let be such that and is shattered. Any total ordering of induces a total ordering over . Since is shattered there is an such that . But then, is the union of at least intervals, a contradiction.

It turns our that a week converse of Proposition 1 also holds. We need a bit of terminology from [14]. A spanning path for is a total ordering of . Its stabbing number is the maximum number of consecutive pairs that a set can stab, i.e., for which . Finally, the stabbing number of is the minimum stabbing number over its spanning paths.

Observation 2

For any split graph , let . The clique-interval number of is up to one the stabbing number of .

The main result of [14] is that every range system of VC-dimension at most has a stabbing number in , for some exponential function . We so obtain:

Corollary 1

For any split graph , let . If has VC-dimension at most then is -clique-interval, for some exponential function .

Interval number.

Finally, we relate the clique-interval number of split graphs with their interval number. A graph is called -interval if we can map every to the union of at most closed interval on the real line, denoted by , in such a way that . In particular, -interval graphs are exactly the interval graphs.

Figure 1: An interval split graph that is not clique-interval.

We observe that there are -interval split graphs that are not -clique-interval, already for . For instance, consider the interval split graph of Fig. 1 (note that is a linear ordering of its maximal cliques). Suppose by contradiction it is clique-interval, and let us consider a corresponding total ordering of . The intersection should be an interval. But then, one of the pairs or is not consecutive, and so one of or is not an interval. Therefore, the graph of Fig. 1 is not clique-interval. Conversely, there are clique-interval split graphs which are not interval graphs. For instance, this is the case of thin spiders, i.e., split graphs such that the edges between the maximal clique and the stable set induce a perfect matching.

Nevertheless we prove a weak connection between the clique-interval number and the interval number of a split graph, by using the VC-dimension. Specifically, Bousquet et al. proved that the neighbourhood set system of any interval graph has VC-dimension at most two [8]. We generalize their result.

Proposition 2

The neighbourhood set system of any -interval graph has VC-dimension at most .

Proof. Let be a -interval graph, and let us fix a corresponding -interval representation. We create a graph whose vertices are the intervals in this representation and such that there is an edge between every two intersecting intervals. Observe that is an interval graph, and so, its neighbourhood system has VC-dimension at most two [8]. Now, let , , be shattered by the neighbourhood set system of . For the corresponding interval set , the cardinality of is an by the Sauer-Shelah-Perles’ Lemma [31, 33]. But then, for any , there are only possibilities for . Since is shattered and, for any , , we so obtain . As a result, we must have that .

Corollary 2

If is a -interval split graph then, is also -clique-interval, for some exponential function .

3 Diameter Computation in quasi linear time

We now address the time complexity of diameter computation on -clique-interval split graphs. Our first result in this section follows from the relations proved in Sec. 2 with the VC-dimension.

Theorem 1[20])

For every , there exists a constant such that in time we can decide whether a graph whose neighbourhood set system has VC-dimension at most has diameter two.

Corollary 3

For every constant , there exists a constant such that in time we can decide whether a -clique-interval split graph has diameter two.

Proof. Let be a -clique-interval split graph. By Theorem 1, it suffices to prove that the neighbourhood set system of has VC-dimension bounded by a function of . The latter system is the union of with . Since the VC-dimension of the union of two set systems can be upper bounded by a function of their respective VC-dimensions [21], we are left proving that both set systems have a VC-dimension which is upper bounded by a function of . By Proposition 1, has VC-dimension at most . Furthermore, since we have for every , the VC-dimension of is up to some constant the same as for . We observe that and are dual from each other. Therefore, the VC-dimension of is at most an exponential of the VC-dimension of  [14]. We conclude from Proposition 1 that the VC-dimension of is at most , and we are done using Theorem 1.

We observe that Corollary 3 also holds for the complements of -clique-interval split graphs – with essentially the same proof as above. In the remainder of this section we focus on the existence of quasi linear-time algorithms. It starts with a small digression on clique-interval split graphs.

3.1 The Case and beyond

Proposition 3

A clique-interval split graph has diameter at most two if and only if it has a universal vertex. In particular, we can compute the diameter of clique-interval split graphs in linear time.

Proof. For clique-interval, let us fix a corresponding total order over the maximal clique . Since is a dominating clique of , all vertices in are at a distance at most two from any other vertex in . Therefore, we are left to decide whether every two vertices in the stable set are at distance two, i.e., whether they have a common neighbour. Equivalently, given the total order over , we must decide whether for every , the intervals and intersect. By the Helly property, a finite family of intervals on the line pairwise intersect if and only if they have a non-empty intersection. As a result, we are left deciding whether , or equivalently whether some vertex of is adjacent to all of . Since is a clique, this is equivalent to having a universal vertex.

We think that our proof of Prop. 3 is a nice introduction to the properties of clique-interval representations for split graphs, that will be further exploited in Sec. 3.2. In the remainder of this part we give an alternative proof of Prop. 3 that also applies to larger subclasses of split graphs. It starts with the following inclusion lemma.

Lemma 2

Every clique-interval split graph is strongly chordal.

Proof. The -sun graph is a split graph such that and for every we have (indices are taken modulo ). A chordal graph is strongly chordal if and only if it does not contain any -sun graph as an induced subgraph, for every  [22]. This is always the case for clique-interval split graphs because, in any -sun graph, the neighbourhoods of the vertices in the stable set induce a cyclic ordering over .

We stress that since every -sun graph is -clique-interval, this above Lemma 2 does not hold for -clique-interval graphs, for any . Now, a maximum neighbour of is any such that (see [9]). We prove that if at least one vertex in a graph has a maximum neighbour then deciding whether the diameter is at most two becomes a trivial task. In particular, this is always the case for strongly chordal graphs [22].

Lemma 3

For every and such that is a maximum neighbour of , we have if and only if is a universal vertex.

Proof. If is universal in then, trivially, . Conversely, assume . Then, for every , , and so there exists a such that . In particular, . As a result, we have or, equivalently, is universal.

Prop. 3 now follows from the combination of Lemmata 2 and 3. Furthermore it turns out that many well-structured graph classes ensure the existence of a vertex with a maximum neighbour such as: graphs with a pendant vertex, threshold graphs [25] and interval graphs [12], or even more generally dually chordal graphs [9]. We conclude that:

Corollary 4

We can compute the diameter of split graphs with minimum degree one and dually chordal split graphs in linear time.

In particular, we can compute the diameter of interval split graphs and strongly chordal split graphs in linear time.

We observe that we can easily modify the hardness reduction from [6] in order to show that, under SETH, we cannot compute in subquadratic time the diameter of split graphs with minimum degree two. Indeed, this aforementioned reduction outputs a split graph such that: is partitioned in two disjoint subsets and ; and a diametral pair must have one end in each subset. Let be fresh new vertices, and let be such that: and:

By construction, if and only if . Therefore, Corollary 4 is optimal for the parameter minimum degree.

3.2 The general case

We are now ready to prove the first main result of this paper.

Theorem 2

If is an -vertex -clique-interval split graph and a corresponding total order over is given then, we can compute the diameter of in time . This is quasi linear-time if .

Conversely, under SETH we cannot compute the diameter of -vertex split graphs with clique-interval number in subquadratic time.

In order to prove Theorem 2, we will use a special instance of -range tree. Such a data-structure stores a static set of -dimensional points and it supports the following operation:

  • (Range Query) Given intervals , compute the number of stored points such that, for every , we have 666 We refer to [13] for a more general presentation of range trees..

Proposition 4 ([13])

For any and any -set of -dimensional points, we can construct a -range tree in time and answer any range query in time .

The use of range queries for diameter computation dates back from [1] (see also [13, 19] for some further applications). Roughly, if in a graph we can find a separator of size at most that disconnects a diametral pair of , then the idea is to compute a “distance profile” for every vertex w.r.t. , and to see this profile as a -dimensional point. We can compute a diametral pair by constructing a -range tree for these points and computing range queries. Our present approach is different from the one in [1] as we define our multi-dimensional points based on some interval representation of the graph rather than on distance profiles, and we use a different type of range query than in [1].

Proof of Theorem 2. Let be a -clique-interval split graph, and a corresponding total order over . By the hypothesis for every , we have is the union of intervals such that . Furthermore since the ordering over is given, the endpoints that delimit these intervals can be computed in time , simply by scanning the neighbours of vertex . We so map every vertex to the -dimensional point , that takes total time . Then, we construct a -range tree for the points , that takes time by Proposition 4.

For every , we are left with computing the number of vertices in at distance two from . Indeed, has diameter at most two if and only if this number is for every . More specifically, for every we want to compute the number of vertices such that:

  1. ;

  2. while for every , ;

  3. and for every and , .

The first and second constraints are equivalent to one of the following three disjoint possibilities:

  • and ;

  • or and ;

  • or .

The third constraint is equivalent to have . Note that in order to subdivide this constraint into disjoint possibilities, it suffices to indicate: (i) the subset of all intervals among that contain an interval for some ; and (ii) the set of all indices such that and are not contained in the same such interval. Overall, that divides the third constraint in at most disjoint events. For a fixed pair we so reduce our computation to at most range queries, that takes time by Proposition 4. Since there are such pairs, the total time in order to compute the number of vertices in at distance two from is an .

Finally, the hardness result for follows from the fact that -treewidth split graphs are -clique-interval (Lemma 1) and that under SETH, we cannot compute the diameter of split graphs with treewidth in subquadratic time [1, 6].

Before ending this section, we give a simpler algorithm for computing the diameter on the complements of -clique-interval split graphs. It is similar in spirit to [20, Lemma 6].

Theorem 3

If is the complement of a -clique-interval split graph and a corresponding total order over is given then, we can compute the diameter of in time .

Proof. By the hypothesis for every , we have is the union of intervals. In particular, is the union of intervals such that . Furthermore since the ordering over is given, the endpoints that delimit these intervals can be computed in time , simply by scanning the neighbours of vertex . Overall, this pre-processing phase takes total time . Then in order to compute the diameter of , for every we store the endpoints for every . This takes time , and so total time . Furthermore a vertex has eccentricity at most two if and only if we have , that is equivalent to have . In order to check whether this collection of intervals covers all of , it suffices to sort the pairs for all and

, and then to scan these ordered pairs from left to right. This can be done in time

. As a result, we can decide whether in total time .

4 Recognition of -Clique-interval Split graphs

Our two algorithms in Sec. 3.2 show that in order to compute the diameter of -clique-interval split graphs in quasi linear time, it is sufficient to compute a corresponding total order of their maximal clique. This raises the question whether such -clique-interval orderings can always be computed in quasi linear time. A first positive example was given by Lemma 1. Indeed, for a split graph of treewidth at most , we can pick any total order of its maximal clique. We complete this easy result by Sec. 4.1 where we give examples of dense subclasses of split graphs with constant clique-interval number and for which a corresponding order can be computed in linear time. Finally, in Sec. 4.2 we prove a stronger result, namely that we can recognize the clique-interval graphs in linear time.

4.1 Examples of subclasses with bounded clique-interval number

A threshold graph is a split graph such that: (i) the neighbourhoods of the vertices in and (ii) the neighbourhoods of the vertices in are totally ordered by inclusion. Observe that threshold graphs can be dense and of unbounded treewidth.

Lemma 4

Every threshold graph is clique-interval.

Proof. For a threshold graph let be such that . In order to prove that is clique-interval, it suffices to construct any total order of such that the subsets are consecutive intervals.

Note that we can easily derive from the proof of Lemma 4 a linear-time algorithm for computing a clique-interval ordering.

Finally, a comparability graph is a graph that admits a transitive orientation.

Lemma 5

For every comparability split graph , we can compute in linear time a total order over such that, for every , is the union of a prefix and a suffix of this order.

In particular, every comparability split graph is -clique-interval.

Proof. A comparability ordering of is a total order over with the property that, for every ,  [26]. For a given comparability graph , we can compute a comparability ordering in linear time [27]. Then, let be the subordering induced by over . For every , we claim that is a (possibly empty) prefix of . Indeed, suppose by contradiction there exist such that . Since we should have , a contradiction. Therefore, the claim is proved. We can prove similarly that is a (possibly empty) suffix of .

We also want to stress that the complements of comparability split graphs, i.e., the cocomparability split graphs are just interval split graphs and we have already considered this case in Section 3.1.

Remark 1. The ordering given by Lemma 5 has some additional properties that can be used for computing the diameter of comparability split graphs in linear time. Indeed, for every such that and are non-empty we have either or because these are prefixes of the ordering over . In particular, and are at distance two from each other. The same holds if both and are non-empty. Hence, let and . We are left deciding whether every are pairwise at distance two. For that, since all the sets are prefixes of the total order over , and in the same way all the sets are suffixes of this ordering, we only need to consider a pair such that and are minimized.

4.2 Linear-time recognition of Clique-interval graphs

Theorem 4

Clique-interval split graphs can be recognized in linear time.

Proof. Let be a split graph. We define a graph from by first transforming into a stable set and then, for every , making a clique of . Furthermore, we claim that is clique-interval if and only if is interval. To see that, let us call clique-path of a graph an ordering of its maximal cliques such that, for any vertex of , the maximal cliques containing are contiguous in the ordering; a graph is interval if and only if it admits a clique-path [4]. We can now prove our claim, as follows:

  • If is clique-interval then, any clique-ordering over for is a total ordering over the maximal cliques , for . Furthermore as already observed in the proof of Proposition 3 the vertices in a subset are pairwise at distance two in if and only if they have a common neighbour in . As a result, the maximal cliques of are exactly the sets , and so admits a clique-path. This implies that is an interval graph.

  • Conversely, if is an interval graph then any clique-path of induces a total ordering over the maximal cliques , and so, a clique-ordering over for .

Unfortunately, computing the graph may take super-linear time. We can overcome this issue as follows. First, given a family of subsets over , if there exists a chordal graph whose maximal cliques are exactly those in then, we can compute a Lex-BFS ordering of in time (Algorithm 10 in [24]). Moreover we can also compute a clique-tree of in time  [34, Sec. 3]. Finally given this Lex-BFS ordering and the corresponding clique-tree of , we can apply Algorithm 9 from [24] in order to decide in time whether is interval. For solving our initial problem, we can take , and in this situation we have .

We left open the status of the recognition of -clique-interval split graphs, for .

5 Open problems

Although the definitions of -clique-interval and -interval split graphs have some similarities, we observe that computing the diameter of -interval split graphs in quasi linear time already looks like a challenging task. Indeed, for a -interval split graph and , the vertices at distance two from are exactly those such that one of their intervals intersects one of the intervals that represent the neighbours of . We cannot use our range query framework in order to avoid overcounting these vertices as this would require up to range queries. More generally, for every fixed , can we compute the diameter of -interval graphs in quasi linear time? We stress that every planar graph is -interval [32], and that the complexity of diameter computation on this class of graphs is a longstanding open problem. The case could thus be an interesting intermediate step.

References

  • [1] A. Abboud, V. Vassilevska Williams, and J. Wang. Approximation and fixed parameter subquadratic algorithms for radius and diameter in sparse graphs. In SODA, pages 377–391. SIAM, 2016.
  • [2] R. Bar-Yehuda, M. Halldórsson, J. Naor, H. Shachnai, and I. Shapira. Scheduling split intervals. SIAM Journal on Computing, 36(1):1–15, 2006.
  • [3] J. H. Bentley. Decomposable searching problems. Information Processing Letters, 8(5):244–251, 1979.
  • [4] Jean R.S. Blair and Barry Peyton. An introduction to chordal graphs and clique trees. The IMA Volumes in Mathematics and its Applications, 56:1–29, 1993.
  • [5] J. A. Bondy and U. S. R. Murty. Graph theory. 2008.
  • [6] M. Borassi, P. Crescenzi, and M. Habib. Into the square: On the complexity of some quadratic-time solvable problems. Electronic Notes in Theoretical Computer Science, 322:51–67, 2016.
  • [7] S. Borgatti and M. Everett. Models of core/periphery structures. Social networks, 21(4):375–395, 2000.
  • [8] N. Bousquet, A. Lagoutte, Z. Li, A. Parreau, and S. Thomassé. Identifying codes in hereditary classes of graphs and VC-dimension. SIAM Journal on Discrete Mathematics, 29(4):2047–2064, 2015.
  • [9] A. Brandstädt, V. Chepoi, and F. Dragan. The algorithmic use of hypertree structure and maximum neighbourhood orderings. Discrete Applied Mathematics, 82(1-3):43–77, 1998.
  • [10] A. Brandstädt, V. Chepoi, and F. Dragan. Distance approximating trees for chordal and dually chordal graphs. Journal of Algorithms, 30(1):166–184, 1999.
  • [11] A. Brandstädt, F. Dragan, H. Le, and V. Le. Tree spanners on chordal graphs: complexity and algorithms. Theoretical Computer Science, 310(1):329 – 354, 2004.
  • [12] A. Brandstädt, C. Hundt, F. Mancini, and P. Wagner. Rooted directed path graphs are leaf powers. Discrete Mathematics, 310(4):897–910, 2010.
  • [13] K. Bringmann, T. Husfeldt, and M. Magnusson. Multivariate analysis of orthogonal range searching and graph distances parameterized by treewidth. In IPEC, 2018.
  • [14] B. Chazelle and E. Welzl. Quasi-optimal range searching in spaces of finite VC-dimension. Discrete & Computational Geometry, 4(5):467–489, 1989.
  • [15] V. Chepoi and F. Dragan. Disjoint sets problem. 1992.
  • [16] D. Corneil, F. Dragan, M. Habib, and C. Paul. Diameter determination on restricted graph families. Discrete Applied Mathematics, 113(2-3):143–166, 2001.
  • [17] Y. Dourisboure and C. Gavoille. Improved compact routing scheme for chordal graphs. In International Symposium on Distributed Computing, pages 252–264. Springer, 2002.
  • [18] F. Dragan. Estimating all pairs shortest paths in restricted graph families: a unified approach. Journal of Algorithms, 57(1):1–21, 2005.
  • [19] G. Ducoffe. A New Application of Orthogonal Range Searching for Computing Giant Graph Diameters. In 2nd Symposium on Simplicity in Algorithms (SOSA 2019), 2019.
  • [20] G. Ducoffe, M. Habib, and L. Viennot. Diameter computation on -minor free graphs and graphs of bounded (distance) VC-dimension. Technical Report 1907.04385, arXiv, 2019.
  • [21] R. Dudley. Central limit theorems for empirical measures.

    The Annals of Probability

    , pages 899–929, 1978.
  • [22] M. Farber. Characterizations of strongly chordal graphs. Discrete Mathematics, 43(2-3):173–189, 1983.
  • [23] M. Golumbic. Algorithmic graph theory and perfect graphs, volume 57. Elsevier, 2004.
  • [24] M. Habib, R. McConnell, C. Paul, and L. Viennot. Lex-BFS and partition refinement, with applications to transitive orientation, interval graph recognition and consecutive ones testing. Theoretical Computer Science, 234(1-2):59–84, 2000.
  • [25] P. Heggernes and D. Kratsch. Linear-time certifying recognition algorithms and forbidden induced subgraphs. Nord. J. Comput., 14(1-2):87–108, 2007.
  • [26] D. Kratsch and L. Stewart. Domination on cocomparability graphs. SIAM Journal on Discrete Mathematics, 6(3):400–417, 1993.
  • [27] Ross M. McConnell and Jeremy P. Spinrad. Modular decomposition and transitive orientation. Discrete Mathematics, 201(1-3):189–241, 1999.
  • [28] R. McGuigan. presentation at NSF-CBMS Conference at Colby College, 1977.
  • [29] S. Olariu. A simple linear-time algorithm for computing the center of an interval graph. International Journal of Computer Mathematics, 34(3-4):121–128, 1990.
  • [30] L. Roditty and V. Vassilevska Williams. Fast approximation algorithms for the diameter and radius of sparse graphs. In STOC, pages 515–524. ACM, 2013.
  • [31] N. Sauer. On the density of families of sets. Journal of Combinatorial Theory, Series A, 13(1):145–147, 1972.
  • [32] E. Scheinerman and D. West. The interval number of a planar graph: Three intervals suffice. Journal of Combinatorial Theory, Series B, 35(3):224–239, 1983.
  • [33] S. Shelah. A combinatorial problem; stability and order for models and theories in infinitary languages. Pacific Journal of Mathematics, 41(1):247–261, 1972.
  • [34] R. Tarjan and M. Yannakakis. Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs. SIAM Journal on Computing, 13(3):566–579, 1984.