Characterizing Star-PCGs

04/09/2018
by   Mingyu Xiao, et al.
Kyoto University
0

A graph G is called a pairwise compatibility graph (PCG, for short) if it admits a tuple (T,w, d_,d_) of a tree T whose leaf set is equal to the vertex set of G, a non-negative edge weight w, and two non-negative reals d_≤ d_ such that G has an edge between two vertices u,v∈ V if and only if the distance between the two leaves u and v in the weighted tree (T,w) is in the interval [d_, d_]. The tree T is also called a witness tree of the PCG G. The problem of testing if a given graph is a PCG is not known to be NP-hard yet. To obtain a complete characterization of PCGs is a wide open problem in computational biology and graph theory. In literature, most witness trees admitted by known PCGs are stars and caterpillars. In this paper, we give a complete characterization for a graph to be a star-PCG (a PCG that admits a star as its witness tree), which provides us the first polynomial-time algorithm for recognizing star-PCGs.

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1 Introduction

Pairwise compatibility graph is a graph class originally motivated from computational biology. In biology, the evolutionary history of a set of organisms is represented by a phylogenetic tree, which is a tree with leaves representing known taxa and internal nodes representing ancestors that might have led to these taxa through evolution. Moreover, the edges in the phylogenetic tree may be assigned weights to represent the evolutionary distance among species. Given a set of taxa and some relations among the taxa, we may want to construct a phylogenetic tree of the taxa. The set of taxa may be a subset of taxa from a large phylogenetic tree, subject to some biologically-motivated constraints. Kearney, Munro and Phillips [12] considered the following constraint on sampling based on the observation in [10]: the pairwise distance between any two leaves in the sample phylogenetic tree is between two given integers and . This motivates the introduction of pairwise compatibility graphs (PCGs). Given a phylogenetic tree with an edge weight and two real numbers and , we can construct a graph each vertex of which is corresponding to a leaf of so that there is an edge between two vertices in if and only if the corresponding two leaves of are at a distance within the interval in . The graph is called the PCG of the tuple . Nowadays, PCG becomes an interesting graph class and topic in graph theory. Plenty of structural results have been developed.

It is straightforward to construct a PCG from a given tuple . However, the inverse direction seems a considerably hard task. Few methods have been known for constructing a corresponding tuple from a given graph . The inverse problem attracts certain interests in graph algorithms, which may also have potential applications in computational biology. It has been extensively studied from many aspects after the introduction of PCG [3, 6, 7, 9, 19, 18].

A natural question was whether all graphs are PCGs. This was proposed as a conjecture in [12], and was confuted in [18] by giving a counterexample of a bipartite graph with with 15 vertices. Later, a counterexample with eight vertices and a counterexample of a planar graph with 20 vertices were found [9]. It has been checked that all graphs with at most seven vertices are PCGs [3] and all bipartite graphs with at most eight vertices are PCGs [14]. In fact, it is even not easy to check whether a graph with a small constant number of vertices is a PCG or not. Whether recognizing PCGs is NP-hard or not is currently open. Some references conjecture the NP-hardness of the problem [7, 9]. A generalized version of PCG recognition is shown to be NP-hard [9].

PCG also becomes an interesting graph class in graph theory. It contains the well-studied graph class of leaf power graphs (LPGs) as a subset of instances such that , which was introduced in the context of constructing phylogenies from species similarity data [8, 13, 15]. Another natural relaxation of PCG is to set . This graph class is known as min leaf power graph (mLPG) [6], which is the complement of LPG. Several other known graph classes have been shown to be subclasses of PCG, e.g., disjoint union of cliques [2], forests [11], chordless cycles and single chord cycles [19], tree power graphs [18], threshold graphs [6], triangle-free outerplanar 3-graphs [16], some particular subclasses of split matrogenic graphs [6], Dilworth 2 graphs [5], the complement of a forest [11] and so on. It is also known that a PCG with a witness tree being a caterpillar also allows a witness tree being a centipede [4]. A method for constructing PCGs is derived [17], where it is shown that a graph consisting two graphs and that share a vertex as a cut-vertex in is a PCG if and only both and are PCGs.

How to recognize PCGs or construct a corresponding phylogenetic tree for a PCG has become an interesting open problem in this area. To make a step toward this open problem, we consider PCGs with a witness tree being a star in this paper, which we call star-PCGs. One motivation why we consider stars is that: in the literature, most of the witness trees of PCGs have simple graph structures, such as stars and caterpillars [7]. It is also fundamental to consider the problem of characterizing subclasses of PCGs derived from a specific topology of trees. Although stars are trees with a rather simple topology, star-PCG recognition is not easy at all. It is known that threshold graphs are star-PCGs (even in star-LPG and star-mLPG) and the class of star-PCGs is nearly the class of three-threshold graphs, a graph class extended from the threshold graphs [6]. However, no complete characterization of star-PCGs and no polynomial-time recognition of star-PCGs are known. In this paper, we give a complete characterization for a graph to be a star-PCG, which provides us the first polynomial-time algorithm for recognizing star-PCGs.

The main idea of our algorithm is as follows. Without loss of generality, we always rank the leaves of the witness star (and the corresponding vertices in the star-PCG ) according to the weight of the edges incident on it. When such an ordering of the vertices in a star-PCG is given, we can see that all the neighbors of each vertex in must appear consecutively in the ordering. This motivates us to define such an ordering to be “consecutive ordering.” To check if a graph is a star-PCG, we can first check if the graph can have a consecutive ordering of vertices. Consecutive orderings can be computed in polynomial time by reducing to the problem of recognizing interval graphs. However, this is not enough to test star-PCGs. A graph may not be a star-PCG even if it has a consecutive ordering of vertices. We further investigate the structural properties of star-PCGs on a fixed consecutive ordering of vertices. We find that three cases of non-adjacent vertex pairs, called gaps, can be used to characterize star-PCGs. A graph is a star-PCG if and only if it admits a consecutive ordering of vertices that is gap-free (Theorem 4.1). Finally, to show that whether a given graph is gap-free or not can be tested in polynomial time (Theorem 5.1), we also use a notion of “contiguous orderings.” All these together contribute to a polynomial-time algorithm for our problem.

The paper is organized as follows. Section 2 introduces some basic notions and notations necessary to this paper. Section 3 discusses how to test whether a given family of subsets of an element set admits a special ordering on , called “consecutive” or “contiguous” orderings and proves the uniqueness of such orderings under some conditions on . This uniqueness plays a key role to prove that whether a given graph is a star-PCG or not can tested in polynomial time. Section 4 characterizes the class of star-PCGs in terms of an ordering of the vertex set , called a “gap-free” ordering, and shows that given a gap-free ordering of , a tuple that represents can be computed in polynomial time. Section 5 first derives structural properties on a graph that admits a “gap-free” ordering, and then presents a method for testing if a given graph is a star-PCG or not in polynomial time by using the result on contiguous orderings to a family of sets. Finally Section 6 makes some concluding remarks. Due to the space limitation, some proofs are moved to Appendix.

2 Preliminaries

For two integers and , let denote the set of integers with . For a sequence of elements, let denote the reversal of . A sequence obtained by concatenating two sequences and in this order is denoted by .

Families of Sets.

Let be a set of elements. We call a subset trivial in if or . We say that a set has a common element with a set if . We say that two subsets intersect (or intersects ) if three sets , , and are all non-empty sets. A partition of is defined to be a collection of disjoint non-empty subsets of such that their union is , where possibly .

Let be a family of subsets of . A total ordering of elements in is called consecutive to if each non-empty set consists of elements with consecutive indices, i.e., is equal to for some . A consecutive ordering of elements in to is called contiguous if any two sets with start from or end with the same element along the ordering, i.e., and satisfy or .

Graphs.

Let a graph stand for a simple undirected graph. A graph (resp., bipartite graph) with a vertex set and an edge set (resp., an edge set between two vertex sets and ) is denoted by (resp., ). Let be a graph, where and denote the sets of vertices and edges in a graph , respectively. For a vertex in , we denote by the set of neighbors of a vertex in , and define degree to be the . We call a pair of vertices and in a mirror pair if . Let be a subset of . Define to be the set of neighbors of , i.e., . Let denote the graph obtained from by removing vertices in together with all edges incident to vertices in , where for a vertex may be written as . Let denote the graph induced by , i.e., .

Let be a tree. A vertex in is called an inner vertex if and is called a leaf otherwise. Let denote the set of leaves. An edge incident to a leaf in is called a leaf edge of . A tree is called a star if it has at most one inner vertex.

Weighted Graphs.

An edge-weighted graph is defined to be a pair of a graph and a non-negative weight function . For a subgraph of , let denote the sum of edge weights in .

Let be an edge-weighted tree. For two vertices , let denote the sum of weights of edges in the unique path of between and .

PCGs.

For a tuple of an edge-weighted tree and two non-negative reals and , define to be the simple graph such that, for any two distinct vertices , if and only if . Note that is not necessarily connected.

A graph is called a pairwise compatibility graph (PCG, for short) if there exists a tuple such that is isomorphic to the graph , where we call such a tuple a pairwise compatibility representation (PCR, for short) of , and call a tree in a PCR of a pairwise compatibility tree (PCT, for short) of . The tree is called a witness tree of . We call a PCG a star-PCG if it admits a PCR such that is a star. Fig. 1 illustrates examples of star-PCGs and PCRs of them. Although phylogenetic trees may not have edges with weight 0 or degree-2 vertices by some biological motivations [4], our PCTs do not have these constraints. This relaxation will be helpful for us to analyze structural properties of PCGs from graph theory. Furthermore, it is easy to get rid of edges with weight 0 or degree-2 vertices in a tree by contracting an edge.

Lemma 1

Every PCG admits a PCR such that and for all edges .

Figure 1: Illustration of examples of star-PCG. (a) A connected and bipartite star-PCG , where ordering is not gap-free to . (b) in (a) with a gap-free ordering . (c) A PCR of in (b). (d) A connected and non-bipartite star-PCG . (e) A PCR of in (d).

3 Consecutive/Contiguous Orderings of Elements

Let be a family of subsets of a set of elements in this section. Let denote the union of all subsets in , and denote the partition of such that for some if and only if has no set with . An auxiliary graph for is defined to be the graph that joins two sets with an edge if and only if and intersect.

3.1 Consecutive Orderings of Elements

Observe that when admits a consecutive ordering of , any subfamily admits a consecutive ordering of . We call a non-trivial set a cut to if no set intersects , i.e., each satisfies one of , and . We call cut-free if has no cut.

Theorem 3.1

For a set of elements and a family of sets, a consecutive ordering of to can be found in time, if one exists. Moreover if is cut-free, then a consecutive ordering of to is unique up to reversal.

3.2 Contiguous Orderings of Elements

We call two elements equivalent in if no set satisfies . We call simple if there is no pair of equivalent elements . Define to be the family of maximal sets such that any two vertices in are equivalent and is maximal subject to this property.

A non-trivial set is called a separator if no other set contains or intersects , i.e., each satisfies or . We call separator-free in if has no separator.

Theorem 3.2

For a set of elements and a family of sets, a contiguous ordering of to can be found in time, if one exists. Moreover all elements in each set appear consecutively in any contiguous ordering of to , and a contiguous ordering of to is unique up to reversal of the entire ordering and arbitrariness of orderings of elements in each set .

4 Star-PCGs

Let be a graph with vertices, not necessarily connected. Let denote the set of mirror pairs in , i.e., , where and are not necessarily adjacent. Let be a star with a center and . An ordering of is defined to be a bijection , and we simply write a vertex with with . For an edge weight in , we simply denote by . When is a star-PCG of a tuple , there is an ordering of such that . Conversely this section derives a necessary and sufficient condition for a pair of a graph and an ordering of to admit a PCR of such that .

Figure 2: Illustration of a gap in an ordered graph : (a) and such that , (b) and such that and , (c) and such that and , where possibly or .

For an ordering of , a non-adjacent vertex pair with in is called a gap (with respect to edges ) if there are edges that satisfy one of the following:
  (g1) and such that (or and
     such that ), as illustrated in Fig. 2(a);
  (g2) and such that and , as illustrated
     in Fig. 2(b); and
  (g3) and such that and , as illustrated
     in Fig. 2(c).
We call an ordering of gap-free in if it has no gap. Clearly the reversal of a gap-free ordering of is also gap-free. We can test if a given ordering is gap-free or not in time by checking the conditions (a)-(c) for each non-adjacent vertex pair in .

Fig. 1(a) and (b) illustrate the same graph with different orderings and , where is not gap-free while is gap-free.

We have the following result, which implies that a graph is a star-PCG if and only if it admits a gap-free ordering of .

Theorem 4.1

For a graph , let be an ordering of . Then there is a PCR of such that if and only if is gap-free.

The necessity of this theorem is relatively easy to prove (see Lemma 9 in the Appendix). Next we consider the sufficiency of Theorem 4.1, which is implied by the next lemma.

Lemma 2

For a graph , let be an gap-free ordering of . There is a PCR of such that . Such a set of weights and bounds can be obtained in time.

Note that when two vertices and are not adjacent in a PCG , there are two reasons: one is that the distance between them in the PCR is smaller than , and the other is that the distance is larger than . Before we try to assign some value to each , we first detect this by coloring edges in the complete graph on the vertex set obtained from a graph by adding an edge between each non-adjacent vertex pair in , where .

For a function , we call an edge with (resp., green and blue) a red (resp., green and blue) edge, and let (resp., and ) denote the sets of red (resp., green and blue) edges. We denote by the set of neighbors of a vertex via red edges. We define and analogously.

A coloring of is defined to be a function such that . When an ordering of is fixed, we simply write (resp., and ) if an edge is a red (resp., green and blue) edge. For and a coloring of , we wish to determine weights , and bounds and so that the next holds:
    ;
    for ;
    for ; and
    for .
To have such a set of values for an ordering and a coloring of , the coloring must satisfy the following conditions:
each admits integers such that

and ,

where if ; if ; and , and if . Such a coloring of is called proper to .

Lemma 3

For a graph and a gap-free ordering of , there is a coloring of that is proper to , which can be found in in time.

Define integers and as follows.

In other words, is the largest with , and , whereas is the smallest with , and . Given a graph , a gap-free ordering of , and a coloring proper to , we can find the set of indices in time. We also compute the set of all mirror pairs in time. Equipped with above results, we can prove the sufficiency of Theorem 4.1 by designing an -time algorithm that assigns the right values to weights in . The details can be found in Appendix 0.E.

5 Recognizing Star-PCGS

Based on Theorem 4.1, we can test whether a graph is a star-PCG or not by generating all orderings of . In this section, we show that testing whether a graph has a gap-free ordering of can be tested in polynomial time.

Theorem 5.1

Whether a given graph with vertices has a gap-free ordering of can be tested in time.

In a graph , let denote the union of edge sets of all cycles of length 3 in , denote the set of end-vertices of edges in , and denote the set of neighbors of a vertex such that .

Figure 3: (a) A disconnected graph , (b) A connected non-bipartite graph , where the edges between two vertices in are not depicted.
Lemma 4

For a graph with a gap-free ordering of and a coloring proper to , let , , and . Then

  1. If two edges and with and cross i.e., or , then they belong to the same component of ;

  2. It holds . The graph is a complete graph, and is a bipartite graph between vertex sets and ;

  3. Every two vertices with satisfy ; and
    Every two vertices with satisfy .

We call the complete graph in Lemma 4(ii) the core of . Based on the next lemma, we can treat each component of a disconnected graph separately to test whether is a star-PCG or not.

Lemma 5

Let be a graph with at least two components.

  1. If admits a gap-free ordering of , then each component of admits a gap-free ordering of its vertex set, and there is at most one non-bipartite component in ; and

  2. Let be a bipartite component of , and . Assume that admits a gap-free ordering of and admits a gap-free ordering of . Then there is an index such that . Moreover, the ordering of is gap-free to .

Proof. (i) Let admit a gap-free ordering of . Any induced subgraph such as a component of is a star-PCG, and a gap-free ordering of its vertex set by Theorem 4.1. By Lemma 4(i), at most one component containing a complete graph with at least three vertices can be non-bipartite, and the remaining graph must be a collection of bipartite graphs.

(ii) Immediate from the definition of gap-free orderings. ∎

We first consider the problem of testing if a given connected bipartite graph is a star-PCG or not. We reduce this to the problem of finding contiguous ordering to a family of sets. For a bipartite graph , define to be the family for the , where even if there are distinct vertices with , contains exactly one set .

For the example of a connected bipartite graph in Fig. 1(a), we have , and .

Lemma 6

Let be a connected bipartite graph with . Then family is separator-free for each , and has a gap-free ordering of if and only if for each , family admits a contiguous ordering of . For any contiguous ordering of , , one of orderings and of is a gap-free ordering to .

Note that . By Theorem 3.2, a contiguous ordering of for each can be computed in time.

Fig. 1(a) illustrates an ordering of of a connected bipartite graph , where consists of a contiguous ordering of and a contiguous ordering of . Although is not gap-free in , the other ordering of that consists of and the reversal of is gap-free, as illustrated in Fig. 1(b).

Finally we consider the case where a given graph is a connected and non-bipartite graph. Fig. 1(d) illustrates a connected and non-bipartite star-PCG whose maximum clique is not unique.

Lemma 7

For a connected non-bipartite graph with , and let be two adjacent vertices in . Let , , and . Assume that has a gap-free ordering of and a proper coloring to such that , . Then:

  1. A maximal clique of that contains edge is uniquely given as . The graph is the core of the ordering , and is a bipartite graph ; and

  2. Let denote the family for , and . Then is a separator-free family that admits a contiguous ordering of , and any contiguous ordering of is a gap-free ordering to .

For example, when we choose vertices and in the connected non-bipartite graph in Fig. 3(b), we have , , and .

For a fixed in Lemma 7, we can test whether the separator-free family in Lemma 7(ii) is constructed from in time by Theorem 3.2, since holds. It takes time to check a given ordering is gap-free or not. To find the right choice of a vertex pair and of some gap-free ordering of , we need to try combinations of vertices to construct according to the lemma. Then we can find a gap-free ordering of a given graph, if one exists in time, proving Theorem 5.1.

By Theorems 4.1 and 5.1, we conclude that whether a given graph with vertices is a star-PCG or not can be tested in time.

6 Concluding Remarks

Pairwise compatibility graphs were initially introduced from the context of phylogenetics in computational biology and later became an interesting graph class in graph theory. PCG recognition is a hard task and we are still far from a complete characterization of PCG. Significant progresses toward PCG recognition would be interesting from a graph theory perspective and also be helpful in designing sampling algorithms for phylogenetic trees. In this paper, we give the first polynomial-time algorithm to recognize star-PCGs. Although stars are trees of a simple topology, it is not an easy task to recognize star-PCGs. For further study, it is an interesting topic to study the characterization of PCGs with witness trees of other particular topologies.

References

  • [1] S. Booth and S. Lueker, Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms, J. Comput. Syst. Sci., 13 (1976) 335–379
  • [2] A. Brandstädt, On leaf powers, Technical report, University of Rostock, 2010.
  • [3] T. Calamoneri, D. Frascaria, and B. Sinaimeri, All graphs with at most seven vertices are pairwise compatibility graphs, The Computer Journal, 56(7) (2013) 882–886
  • [4] T. Calamoneri, A. Frangioni, and B. Sinaimeri, Pairwise compatibility graphs of caterpillars, The Computer Journal, 57(11) (2014) 1616–1623
  • [5] T. Calamoneri and R. Petreschi, On pairwise compatibility graphs having Dilworth number two, Theoret. Comput. Sci., 524 (2014) 34–40
  • [6] T. Calamoneri, R. Petreschi, and B. Sinaimeri, On the pairwise compatibility property of some superclasses of threshold graphs, Discrete Math. Algorithms Appl., 5(2) (2013), article 360002
  • [7] T. Calamoneri and B. Sinaimeri, Pairwise compatibility graphs: A survey, SIAM Review 58(3)(2016) 445–460
  • [8] Z.-Z. Chen, T. Jiang, and G. Lin, Computing phylogenetic roots with bounded degrees and errors, SIAM J. Comput., 32 (2003) 864–879
  • [9] S. Durocher, D. Mondal, and Md. S. Rahman, On graphs that are not PCGs, Theoret. Comput. Sci., 571 (2015) 78–87
  • [10] J. Felsenstein, Cases in which parsimony or compatibility methods will be positively misleading, Systematic Zoology, 27 (1978) 401–410
  • [11] Md. I. Hossain, S. A. Salma, Md. S. Rahman, and D. Mondal, A necessary condition and a sufficient condition for pairwise compatibility graphs, J. Graph Algorithms Appl. 21(3) (2017) 341–352
  • [12] P. E. Kearney, J. I. Munro, and D. Phillips, Efficient generation of uniform samples from phylogenetic trees, in Algorithms in Bioinformatics, G. Benson and R. Page, eds., LNCS 2812, Springer, Berlin, Heidelberg, 2003, 177–189
  • [13] G. Lin, P. E. Kearney, and T. Jiang, Phylogenetic k-root and Steiner k-root, in Algorithms and Computation, G. Goos, J. Hartmanis, J. van Leeuwen, D. T. Lee, and S.-H. Teng, eds., LNCS 1969, Springer, Berlin, Heidelberg, 2000, 539–551
  • [14] S. Mehnaz and Md. S. Rahman, Pairwise compatibility graphs revisited, in Proceedings of the 2013 International Conference on Informatics, Electronics Vision (ICIEV), 2013, 1–6
  • [15] N. Nishimura, P. Ragde, and D. M. Thilikos, On graph powers for leaf-labeled trees, J. Algorithms, 42 (2002) 69–108
  • [16] S. A. Salma, Md. S. Rahman, and Md. I. Hossain, Triangle-free outerplanar 3-graphs are pairwise compatibility graphs, J. Graph Algorithms Appl., 17 (2013) 81–102
  • [17] M. Xiao and H. Nagamochi, Some reduction operations to pairwise compatibility graphs, Technical report 2017-003, December 1, 2017, http://www-or.amp.i.kyoto-u.ac.jp/members/nag/Technicalreport/TR2017-003.pdf
  • [18] M. N. Yanhaona, Md. S. Bayzid, and Md. S. Rahman, Discovering pairwise compatibility graphs. Discrete Math., Alg. and Appl. 2(4)(2010) 607–624
  • [19] M. N. Yanhaona, K. S. M. T. Hossain, and Md. S. Rahman, Pairwise compatibility graphs, in: WALCOM 2008, 2008, 222–233

Appendix 0.A Proof of Lemma 1

Lemma 1 Every PCG admits a PCR such that and for all edges .

Proof. Since the case where a given PCG has at most two vertices is trivial, we assume that has at least three vertices. Let be a PCR of , where each path between two leaves in contains exactly two leaf edges since . We increase some values of , and and so that the resulting tuple satisfies the lemma. For two positive reals and , let for each leaf edge , for all non-leaf edges , , and . We observe that is a PCR of satisfying the lemma because each path between two leaves in contains exactly two leaf edges ∎

Appendix 0.B Proof of Theorem 3.1

First we prove the time complexity of the theorem. A graph with vertices is called an interval graph if each vertex

can represented by an ordered pair

of reals so that two vertices are adjacent if and only if there is a real such that two intervals and . It is know that testing if a given graph is an interval graph and finding such a representation , , if one exists can be done in time [1]. Given a family , we see that admits a consecutive ordering if and only if the auxiliary graph for is an interval graph by the definition of . The time to construct from is since we can check in time whether two sets intersect, i.e., vertices are adjacent in . Clearly and