# Disimplicial arcs, transitive vertices, and disimplicial eliminations.

In this article we deal with the problems of finding the disimplicial arcs of a digraph and recognizing some interesting graph classes defined by their existence. A diclique of a digraph is a pair V [right arrow] W of sets of vertices such that v [right arrow] w is an arc for every v [member of] V and w [member of] W. An arc v [right arrow] w is disimplicial when it belongs to a unique maximal diclique. We show that the problem of finding the disimplicial arcs is equivalent, in terms of time and space complexity, to that of locating the transitive vertices. As a result, an efficient algorithm to find the bisimplicial edges of bipartite graphs is obtained. Then, we develop simple algorithms to build disimplicial elimination schemes, which can be used to generate bisimplicial elimination schemes for bipartite graphs. Finally, we study two classes related to perfect disimplicial elimination digraphs, namely weakly diclique irreducible digraphs and diclique irreducible digraphs. The former class is associated to finite posets, while the latter corresponds to dedekind complete finite posets.Keywords: disimplicial arcs, bisimplicial edges of bipartite graphs, disimplicial elimination schemes, bisimplicial elimination schemes, diclique irreducible digraphs, transitive digraphs, dedekind digraphs

1 Introduction

Disimplicial arcs are important when Gaussian elimination is performed on a sparse matrix, as they correspond to the entries that preserve zeros when chosen as pivots. Let M be an n x n matrix and G(M) be the digraph that has a vertex [r.sub.i] for each row of M and a vertex [c.sub.j] for each column of M, where [r.sub.i] [right arrow] [c.sub.j] is an arc of G(M) if and only if [m.sub.ij] [not equal to] 0. The fill-in of [m.sub.ij] is the number of zero entries of M that change into a non-zero value when [m.sub.ij] is the next pivot. To reduce the extra space required to represent M, the idea is to pivot with an entry of minimum fill-in. The extreme case in which [m.sub.ij] has zero fill-in happens when [m.sub.xy] [not equal to] 0 for every x,y such that [m.sub.iy] [not equal to] 0 and [m.sub.xj] [not equal to] 0. Translated to G(M), the arc [r.sub.i] [right arrow] [c.sub.j] has "zero fill-in" if and only if [r.sub.x] [right arrow] [c.sub.y] is an arc of G(M) for every x,y such that [r.sub.i] [right arrow] [c.sub.y] and [r.sub.x] [right arrow] [c.sub.j] are arcs of G(M). In graph theoretical terms, the arcs with "zero fill-in" are the disimplicial arcs of G(M), i.e., the arcs that belong to a unique maximal diclique of G(M).

The discussion above is usually described in terms of bisimplicial edges of bipartite graphs, and not in terms of the disimplicial arcs of digraphs. We emphasize that these concepts are equivalent for G(M). Say that a digraph is a source-sink (ST) graph when every vertex is either a source or a sink. Clearly, there are two ST graphs for every bipartite graph G = (V,W,E), depending on whether the edges are oriented from V to W or from W to V. Moreover, there is a one-to-one correspondence between the bisimplicial edges of G and the disimplicial arcs of its orientations. Thus, it is unimportant whether G(M) is oriented or non-oriented. There is a reason why we work with digraphs in this manuscript that has to do with the fact that we relate the disimplicial arcs of ST graphs with the vertices of transitive digraphs. So, in this way we need not describe how the edges of a non-oriented graph should be oriented.

Finding the disimplicial arcs of a digraph D is an interesting and somehow unexplored problem. It is rather simple to determine if an arc is disimplicial in O(m) time, thus all the disimplicial arcs can be obtained in O([m.sup.2]) time and O(m) space. (We use n and m to denote the number of vertices and arcs of D. Also, we assume D is connected, hence m [greater than or equal to] n - 1.) As we shall see in Section 3, this problem can be reduced to that of finding the disimplicial arcs of an ST graph G. As it was noted by Bomhoff and Manthey [2], the twin reduction G' of G can have at most [tau] disimplicial arcs, where [tau] < n is the number of thin arcs of G'. This yields an O([tau]m) time and O(m) space algorithm to find all the disimplicial arcs of G. Bomhoff and Manthey also show that certain random graphs have a constant number of thin arcs, in which case the algorithm takes linear time. Fast matrix multiplication can also be used to obtain the disimplicial arcs, but at the expense of [THETA]([n.sup.2]) space. This algorithm is, therefore, not convenient for G sparse.

In the process of Gaussian elimination not only the next pivot is important; the whole sequence of pivots is of matter. Ideally, we would like to use no extra space throughout the algorithm to represent the input matrix M. Thus, no zero entry of M should be changed into a non-zero entry in the entire elimination process. Golumbic and Goss [6] observed that this problem corresponds to finding a perfect elimination scheme of G(M). An elimination scheme of a digraph G is a sequence of arcs S = [v.sub.1] [right arrow] [w.sub.1],..., [v.sub.k] [right arrow] [w.sub.k] such that [v.sub.i] [right arrow] [w.sub.i] is disimplicial in G \ {[v.sub.1], [w.sub.1],..., [v.sub.i-1], [w.sub.i-1]}, for every 1 [less than or equal to] i [less than or equal to] k. The sequence S is maximal when G \ V(S) has no disimplicial arcs, while it is perfect when G \ V(S) has no edges at all. Not every digraph admits a perfect elimination scheme; those that do admit it are said to be perfect elimination. In [6] it is proven that every maximal elimination scheme of G is perfect when G is a perfect elimination ST graph.

The first algorithm to compute a maximal elimination scheme of an ST graph was given by Golumbic and Goss in the aforementioned article. The algorithm works by iteratively removing the endpoints of a disimplicial arc until no more disimplicial arcs remain. The complexity of their algorithm is not explicit in [6]; if the disimplicial arcs are searched for as in [2], then O([tau]nm) = O([n.sup.2]m) time and O(m) space is required. Goh and Rotem [5] propose an O([n.sup.3]) time and O([n.sup.2]) space algorithm, which was later improved by Bomhoff so as to run in O(nm) time [1]. For the densest cases, the algorithm by Spinrad [12] runs in O([n.sup.3]/ log n) time and O([n.sup.2]) space. Bomhoff [1] shows the most efficient algorithm for the sparse case up to this date, requiring O([m.sup.2]) time while consuming O(m) space.

A common restriction of the zero fill-in problem is to ask all the pivots to belong to the diagonal of M. This problem is equivalent to that of finding a perfect elimination scheme whose arcs all belong to some input matching E of G(M). The matching E represents the arcs that correspond to the diagonal entries of M. Again, this problem can be solved by finding an elimination scheme S [??] E such that no arc of E S is disimplicial in G(M) \ V(S) [6]. Rose and Tarjan [10] devise two algorithms for finding such an elimination scheme of an ST graph, one runs in O(nm) time and space, and the the other requires O([n.sup.2]m) time but consumes only O(m) space. The O([m.sup.2]) time algorithm by Bomhoff for finding an unrestricted scheme works in O(nm) time and O(m) space for this case.

Problem Existing algorithms Proposed algorithms (ST graphs) (general digraphs) List disimplicial arcs O(nm) [2] O([alpha]m) Disimplicial elimination O([m.sup.2]) [1] O(min{[eta][DELTA], m}m) M-disimplicial elimination O(nm) [1] O([alpha]m) WDI recognition O([alpha]m) DI recognition O(nm) Tab. 1: Time complexities of the proposed algorithms for sparse digraphs; all the proposed algorithms require linear space.

In this manuscript we consider two classes related to perfect elimination digraphs, namely diclique irreducible and weakly diclique irreducible digraphs. As far as our knowledge extends, these classes were not studied previously. The motivating question is when does an ST graph G admit a perfect matching E of disimplicial arcs. For such graphs, any permutation of E is a perfect elimination scheme, thus the pivots of the matrix associated to G can be taken in any order from E with zero fill-in. How to answer this question efficiently is already known, as it reduces to establishing if the thin arcs form a perfect matching of disimplicial arcs (see [2] and Section 3). Nevertheless, the class defined by these graphs has some interesting properties. Note that, by definition, the arc set of G can be partitioned into a family of dicliques, all of which contain a disimplicial arc. This resembles the definition of weakly clique irreducible graphs [14], in which every edge should belong to a clique that contains a simplicial edge. For this reason is that we say a digraph G is weakly diclique irreducible (WDI) when every arc of G belongs to a diclique that contains a disimplicial arc. The word "weakly" in the definition of weakly clique irreducible graphs comes from the fact that this is a superclass of the clique irreducible graphs. A graph is clique irreducible when every maximal clique has a simplicial edge [13]. By analogy, we define the diclique irreducible (DI) digraphs as those digraphs in which every maximal diclique has a disimplicial arc.

In this manuscript we develop new algorithms for the above problems on general digraphs. We are mainly interested in the case in which the input digraph is sparse, and its sparseness is "well distributed". By this, we mean that we expect each subdigraph to be sparse as well. The arboricity [alpha] of a digraph correctly measures this kind of density, as it is the maximum value e/p for a subdigraph with e arcs and p + 1 vertices [9]. So, rephrasing, we are mainly interest in the case in which [alpha] << n/2. Sometimes, however, our algorithms are most efficient when the input digraph is sparse in a stronger sense, as it must have low h-index or low maxdegree. The h-index is the maximum [eta] such that the digraph has [eta] vertices with degree at least [eta], while the maxdegree [DELTA] is the maximum among the degrees of the vertices; it is well known that [alpha] [less than or equal to] [eta] [less than or equal to] [DELTA] (see e.g. [8]). Table 1 summarizes the improvements with respect to the best known algorithms previously described.

The article is organized as follows. In Section 2 we introduce the terminology used. In Section 3 we show two simple operators that transform disimplicial arcs into transitive vertices and back. As a consequence, finding the disimplicial arcs and finding the transitive vertices are equally hard problems. In particular, an O(min{[alpha], [tau]}m) time and O(m) space algorithm for a digraph with [tau] thin arcs is obtained, improving over the algorithm in [2]. This algorithm is optimal unless an o([alpha]m) time algorithm for finding the transitive vertices of a sparse graph is obtained, which is an open problem [11]. In Section 4 we study the problem of generating maximal elimination schemes. For the general case we show an algorithm that runs in O(min{[eta][DELTA], m}m) time and O(m) space. The improvement with respect to the algorithm in [1] is significant for graphs with [??]For the case in which all the arcs of the elimination scheme must belong to an input matching, we develop an O([alpha]m) time and O(m) space; which is a major improvement for sparse graphs. The classes of WDI and DI digraphs are studied in Section 5. We show that the operators of Section 3 provide a bijection f between a subfamily of WDI digraphs and finite posets. When DI digraphs are considered, the range of f are precisely the dedekind complete finite posets, i.e., the finite posets that satisfy the least upper bound property. With respect to the recognition problems, it can be solved in O([alpha]m) time for WDI digraphs and in O(nm) time for DI digraphs. Finally, in Section 6 we translate all the results to bipartite graphs while we provide further remarks.

2 Preliminaries

A digraph is a pair D = (V(D),E(D)) where V(D) is finite and [??] V(D) and E(D) are the vertex set and arc set of D, respectively. We write v[right arrow]w to denote the arc with endpoints v and w that leaves v and enters w, regardless of whether (v, w) [member of] E(D) or not. Note that our definition allows D to have an arc v[right arrow]v for any v [member of] V(D); in such case, v is a reflexive vertex and v[right arrow]v is a loop. For [??], we write D[V] to denote the subdigraph of D induced by V, and D \ V to denote D[V(D)\V].

For v [member of] V(D), define N+(v) = {w [member of] V(D) |v [right arrow] w [member of] E(D)}, [N.sup.-.sub.D](v) = {w [member of] V(D) | w [right arrow] v [member of] E(D)}, and ND (v) = [N.sup.+.sub.D](v)[union] [N.sup.-.sub.D](v). Sets [N.sup.+.sub.D](v), [N.sup.-.sub.D](v), and [N.sub.D](v) are the out-neighborhood, in-neighborhood, and neighborhood of v in D, respectively, while the members of [N.sup.+.sub.D](v), [N.sup.-.sub.D](v), and [N.sub.D](v) are the out-neighbors, in-neighbors, and neighbors of v, respectively. The out-degree, in-degree, and degree of v are the values [d.sup.+.sub.D](v) = |[N.sup.+.sub.D](v)|, [d.sup.-.sub.D]v) = |[N.sup.-.sub.D](v)|, and [d.sub.D](v) = |[N.sub.D](v)|, respectively. We omit the subscript from N and d whenever D is clear from context.

For v [member of] V(D), we say that v is a source when [d.sup.-](v) = 0, v is a sink when [d.sup.+](v) = 0, and v is transitive when x [right arrow] y [member of] E(D) for every x [member of] [N.sup.-](v) and y [member of] [N.sup.+](v). A digraph is a source-sink (ST) graph when it contains only source and sink vertices, while it is transitive when it contains only transitive vertices. A digraph is simple when it has no loops, while it is reflexive when every vertex is reflexive. The reflexive closure of D is the digraph obtained by adding all the missing loops to D so as to make each vertex reflexive, i.e., the reflexive closure of D is (V(D), E(D) [union] {(v,v) | v [member of] V(D)}). An oriented graph is a digraph such that v[right arrow]w [member of] E(D) and w[right arrow]v [member of] E(D) only if v = w. An order graph is an oriented graph that is simultaneously reflexive and transitive. Let [less than or equal to] be the relation on V(D) such that v [less than or equal to] w if and only if v[right arrow]w [member of] E(D). Note that [less than or equal to] is reflexive (resp. antisymmetric, transitive) precisely when D is reflexive (resp. oriented, transitive). Thus, D is an order graph if and only if (V(D), [less than or equal to]) is a finite poset.

For v [member of] V(D), we write [H.sup.+.sub.D](v) = {w [member of] [N.sup.+.sub.D](v) | [d.sup.+](v) [less than or equal to] [d.sup.-](w)} and [H.sup.-.sub.D](v) = {w [member of] [N.sup.-.sub.D](v) | [d.sup.-](v) [less than or equal to] d+(w)}. In other words, [H.sup.+.sub.D](v) has the out-neighbors of v whose in-degree is greater than or equal to the out-degree of v, while [H.sup.-.sub.D] (v) has the out-neighbors of v with in-degree at least [d.sup.-] (v). Note that either v [member of] [H.sup.-](w) or w [member of] [H.sup.+](v) for every arc v[right arrow]w [member of] E(D), thus all the arcs of D get visited when all the H sets are traversed. The values |[H.sup.+.sub.D](v)|, |[H.sup.-.sub.D](v)| are denoted by [h.sup.+.sub.D](v) and [h.sup.-.sub.D](v), while [h.sub.D](v) = max{[h.sup.+.sub.D](v), [h.sup.-.sub.D](v)}. Again, we omit the subscript D when no ambiguities arise.

We write [n.sub.D], [m.sub.D], and [[DELTA].sub.D] to denote the values |V(D)|, |E(D)|, and [max.sub.v[member of]V(D)]{d(v)}, respectively. The arboricity and h-index are values that measure how dense is a digraph. We use a non-standard definition of arboricity given by the equivalence in [9], i.e., the arboricity [[alpha].sub.D] of D is the maximum e/p such that D has a subdigraph with e arcs and p + 1 vertices. The h-index is the value [[eta].sub.D] such that D has [[eta].sub.D] vertices with degree at least [[eta].sub.D]. It is well known that [[alpha].sub.D] [less than or equal to][[eta].sub.D][LESS THAN OR EQUAL TO] min{[DELTA], [??], while h(v) [less than or equal to] [[eta].sub.D] for every v [member of] V(D) [3, 8]. The time required to multiply two n x n matrices is denoted by O([n.sup.[omega]]); up to this date 2 [less than or equal to] [omega] [less than or equal to] 2.3728639 [7]. As before, we omit the subscripts D whenever possible. Also, we assume m > n for all the problems considered with no loss of generality.

Two arcs of D are independent when they have no common endpoints. A matching is a set M of pairwise independent arcs. Sometimes we deal with M as if it were the subgraph of D with vertex set {v, w |v [right arrow] w [member of] M} and arc set M. Thus, we write V(M) to denote the set of vertices entering or leaving an arc of M, or we talk about the unique neighbor of v in M, etc. A matching is perfect when V(M)=V(G).

A diclique of D is an ordered pair (V, W) [??] V(D) x V(D) such that v[right arrow]w [member of] E(D) for every v [member of] V and w [member of] W (note that every vertex in V [intersection] W is reflexive). For the sake of notation, we write V[right arrow]W to refer to any ordered pair (V, W) with V, W [??] V(G), regardless of whether (V, W) is a diclique or not. The term diclique is also used to denote the subdigraph B of D with vertex set V [union] W and arc set {v[right arrow]w \ v [member of] V, w [member of] W}; note that B needs not be an induced subdigraph of D. Thus, for instance, we can talk about the arcs of the diclique B. A diclique V[right arrow]W of D is maximal when D has no diclique V [union] V[right arrow]W U W' for [??] [subset] V' [union] W' [??] V(D). An arc v[right arrow]w [member of] E(D) is disimplicial when B = [N.sup.-] (w) [right arrow] [N.sup.+] (v) is a diclique of D; note that B is the unique maximal diclique of D that contains v[right arrow]w. In such case, the diclique B is said to be reduced, i.e., B is reduced when it is maximal and it contains a disimplicial arc.

3 Disimplicial arcs versus transitive vertices

By definition, a reflexive vertex v is transitive if and only if v[right arrow]v is a disimplicial arc. Hence, we can find out if a digraph D is transitive by looking if all the loops of its reflexive closure D* are disimplicial. This result can be easily strengthen so as to make D* an ST graph.

For any digraph D, define Split(D) to be the digraph G that has a vertex out(v) for each non-sink vertex v, and a vertex in(w) for each non-source vertex w, where out(v)[right arrow]in(w) [member of] E(G) if and only if v[right arrow]w [member of] E(D), for every v, w [member of] V(D) (see Figure 1). Clearly, out(v) and in(w) are source and sink vertices, resepctively, hence G is an ST graph. Moreover, the dicliques of D are "preserved" into G as in the next proposition.

Proposition 1 Let D be a digraph. Then, V [right arrow] W is a diclique of D if and only if out(V)[right arrow]in(W) is a diclique of Split(D), where out(V) = {out(v) | v [member of] V} and in(W) = {in(w) | w [member of] W}.

Note that disimplicial arcs are preserved as well; indeed, V[right arrow]W is the unique diclique containing v[right arrow]w if and only if out(V)[right arrow]in(W) is the unique diclique containing out(v)[right arrow]in(w).

Corollary 2 Let D be a digraph. Then, v[right arrow]w is a disimplicial arc of D if and only if out(v)[right arrow]in(w) is a disimplicial arc of Split(D).

So, as anticipated, we can find out whether D is transitive or not by computing the disimplicial arcs of Split(D*). Since Split(D*) can be computed in linear time when D is provided as input, we conclude that finding the disimplicial arcs of an ST graph is at least as hard as testing if a digraph is transitive. That is, any algorithm that lists the disimplicial arcs of an ST graph in O(t) time and O(s) space can be transformed into an algorithm that tests if a digraph is transitive in O(t) time and O(s) space.

[FIGURE 1 OMITTED]

Theorem 3 A digraph D is transitive if and only if all the arcs in the matching {out(v) [right arrow] in(v) | v [member of] V(D*)} of Split(D*) are disimplicial, where D* is the reflexive closure of D.

For the rest of this section, we discuss how to find disimplicial arcs by computing transitive vertices. The idea is to revert, as much as possible, the effects of Split. For any matching M of a digraph G, define Join(G, M) to be the digraph D that has a vertex (v, v) for each v [member of] V(G) \ V(M), and a vertex (v, w) for each v [right arrow] w [member of] M, where (v, w) [right arrow] (x, y) [member of] E(D) if and only if v [right arrow] y [member of] E(G) (see Figure 1). The restricted duality between the Split and Join operators is given in the next lemmas.

Lemma 4 If D is a reflexive digraph, then D is isomorphic to Join(Split(D), {out(v) [right arrow] in(v) | v [member of] V(D)}).

Proof: Note that M = {out(v) [right arrow] in (v) | v [member of] V(D)} is a perfect matching of Split(D) because D is reflexive, hence H = Join(G, M) is well defined for G = Split(D). Let f : V(D) [right arrow] V(H) be the function such that f(v) = (in(v), out(v)) (see Figure 2). By definition of Split, v [right arrow] w [member of] E(D) if and only if out(v) [right arrow] in(w) [member of] E(G), for every v, w [member of] V(D). Similarly, by the definition of Join, out(v) [right arrow] in(w) [member of] E(G) if and only if (out(v), in(v)) [right arrow] (out(w), in(w)) [member of] E(H). That is, v [right arrow] w [member of] E(D) if and only if f(v) [right arrow] f(w) [member of] E(H).

Lemma 5 If M is a perfect matching of an ST graph G, then G is isomorphic to Split(Join(G, M)).

Proof: The proof is analogous to that of Lemma 4. This time, take H = Split(Join(G, M)) and m(v) be the neighbor of v in M, and observe that f: V(G) [right arrow] V(H) is an isomorphism when f(v) = in((v, m(v))) for every sink vertex v and f(v) = out((m(v), v)) for every source vertex v.

[FIGURE 2 OMITTED]

Despite Lemma 5 requires an ST graph G with a perfect matching M, the Join operator can be applied to any digraph and any matching. The final result is always the same, though; the disimplicial arcs of M get transformed into transitive vertices.

Theorem 6 Let M be a matching of a digraph G, and v [right arrow] w [member of] M. Then, v [right arrow] w is disimplicial in G if and only if(v, w) is a transitive vertex of Join(G, M).

Proof: Let D = Join(G, M) and observe that (v,w) [member of] V(D). By definition, (a, b) [right arrow] (x,y) [member of] E(D) if and only if a [right arrow] y [member of] E(G), for every a, b,x,y [member of] V(G). Then, (a, b) [right arrow] (x, y) [member of] E(D) for every pair (a, b), (x, y) [member of] V (D) such that (a, b) [right arrow] (v, w) [member of] E(D) and (v, w) [right arrow] (x, y) E(D) if and only if a [right arrow] y [member of] E(G) for every pair a, y [member of] V(G) such that a [right arrow] w [member of] E(G) and v[right arrow]y [member of] E(G). That is, (v, w) is transitive in D if and only if v[right arrow]w is disimplicial in G.

Theorem 6 gives us a method for testing if an arc v [right arrow] w is disimplicial: check if (v, w) is transitive in D = Join(G, {v [right arrow] w}). Since D can be computed in O([d.sub.G](v) + [d.sub.G](w)) time when G and v [right arrow] w are given as input, we conclude that querying if an arc is disimplicial is equally hard as determining if a vertex is transitive. We remark that testing if (v,w) [member of] V(D) is transitive and checking if v [right arrow] w [member of] E(G) is disimplicial are both solvable in O(m) time.

Theorem 6 can also be used to find all the disimplicial arcs of G when an adequate matching is provided. For the sake of simplicity, we restrict ourselves to ST graphs, by Proposition 1. Moreover, we find it convenient to eliminate twin vertices. Two vertices v, w of an ST graph G are twins when N(v) = N(w), while G is twin-free when it contains no pair of twins. A twin block is a maximal set of twin vertices; note that V(G) admits a unique partition into twin blocks. We assume the existence of a function [repr.sub.G] that, given a block B, returns a vertex of B, and we write [repr.sub.G](v) = [repr.sub.G](B) for every v [member of] B. For the sake of notation, we omit the subscript G from repr when no ambiguities arise. The twin reduction of G is the subdigraph Repr(G) of G induced by {repr(B) | B is a block of G}. The twin reduction of G contains all the information about the disimplicial arcs of G, as in the next proposition.

Proposition 7 An arc v [right arrow] w of an ST graph G is disimplicial if and only if repr(v) [right arrow] repr(w) is disimplicial in Repr(G).

We are now ready to state what an adequate matching looks like. For each v [member of] V(G), define the thin neighbor [theta](v) of v to be the (unique) vertex w [member of] N(v) such that d(w) < d(z) for every z [member of] N(v) \ {w}; if such a vertex does not exist, then [theta](v) is some undefined vertex. Say that an arc v[right arrow]w is thin when v = [theta](w) and w = [theta](v). For the sake of notation, we write Join(G) to denote Join(G, M) where M is the set of thin arcs of G; note that Join(G) is well defined because M is a matching. The following easy-to-prove lemma is as fundamental for us as it is for the algorithm in [2].

Lemma 8 (see e.g. [2]) All the disimplicial arcs of a twin-free ST graph are thin.

The algorithm to compute the disimplicial arcs of an ST graph works in two phases. In the first phase, all the disimplicial arcs of H = Repr(G) are obtained by querying which of the vertices of Join(H) are transitive. In the second phase, each v[right arrow]w [member of] E(G) is tested to be disimplicial by querying if repr(v)[right arrow]repr(w) is disimplicial in H. The algorithm is correct by Theorem 6, Proposition 7, and Lemma 8.

Theorem 9 An arc v[right arrow]w of a digraph G is disimplicial if and only if (repr(out(v)), repr(in(w))) is transitive in Join(Repr(Split(G))).

Since Split, Join, and Repr can be computed in linear time, we conclude that listing the disimplicial arcs and finding the transitive vertices are equally hard problems. Up to this date, the best algorithms for computing the transitive vertices of D = Join(H) take O([[alpha].sub.D][m.sub.D]) time and O([m.sub.D]) space or O([n.sup.[omega].sub.D]) time and O([n.sup.2.sub.D]) space. Since [[alpha].sub.D] = O([[alpha].sub.G]), [n.sub.D] = O([n.sub.G]), and [m.sub.D] = O([m.sub.G]), we conclude that the disimplicial arcs of a digraph G can be obtained in either O([[alpha].sub.G][m.sub.G]) time and O([m.sub.G]) space or O([n.sup.[omega].sub.G]) time and O([n.sup.2.sub.G]) space.

4 Disimplicial eliminations

The present section is devoted to the problems of finding disimplicial elimination sequences. Before doing so, we review the h-digraph structure as it is required by our algorithms.

The h-graph structure was introduced in [8] with dynamic algorithms in mind. It proved to be well suited for some vertex elimination problems, particularly those in which the conditions for removing a vertex are local to its neighborhood. The h-digraph structure is the cousin of h-graphs for digraphs, and it was superficially described in [8]. Let D be a digraph and {*, o} = {+, -}. In short, the h-digraph structure maintains 3 values for * and each v [member of] V(D), namely d*(v), N*(v), and H*(v), where N* is an ordered list of the nonempty sets N*(v, i) = {z [member of] N*(v) | do(z) = i} with i < d*(v). Recall that H* = {z [member of] N*(v) | do(z) [greater than or equal to] d*(v)}. The data structure also keeps track of several pointers that allow efficient access to the different incarnations of a vertex in the structure (see [8]). At all, no more than O(m) bits are consumed.

Table 2 describes the operations supported by the h-digraph structure that are of interest for our purposes. All of them, but MinN, where described in [8] for graphs, though their translation to digraphs is direct. For the implementation of MinN, two cases are considered to obtain the desired output L. If N* = [??], then d*(v) [less than or equal to] do(w) for every w [member of] N*(v), thus L [??] H*(v); otherwise, L is equal to the first set in N* (v). The time required for this operation is, therefore, O(h(v)).

4.1 General disimplicial eliminations

A sequence of arcs S = [v.sub.1] [right arrow] [w.sub.1],...,[v.sub.k][right arrow][w.sub.k] is a disimplicial elimination of a digraph G when [v.sub.i][right arrow][w.sub.i] is disimplicial in G \ {[v.sub.1], [w.sub.1],..., [v.sub.i-1],[w.sub.i-1.sub.]} for every 1 [less than or equal to] i [less than or equal to] k; S is maximal when G \ {[v.sub.1], [w.sub.1],..., [v.sub.k], [w.sub.k]} has no disimplicial arcs. For convenience, we write V(S) = {[v.sub.1], [w.sub.1],..., [v.sub.k], [w.sub.k]}. Operation

Operation Description Complexity one all Initialize(D) creates the h-graph structure of D - O ([alpha]m) Remove(v, D) removes v [member of] V(D) from D O(dh) O([alpha]m) N' (v, D, *) returns {w [right arrow] O(dh) O([alpha]m) z [member of] E(D) |w, z [member of] N* (v)} MinN(v, D, *) returns {w [member of] N* O(h) - (v) | do(w) [less than or equal to] do(z) for z [member of] N*(v)} d(v,D,*) returns d* (v) O(1) - Tab. 2: Some operations supported by the h-digraph data structure. The complexity column "one" indicates the time required by one invocation of the operation, while the complexity column "all" indicates the time required when the operation is applied O(1) times to all the vertices in the digraph. Here h = h(v), d = d(v), [alpha] = [[alpha].sub.D] and m = [m.sub.D], * must belong to {+, -}, and o is the opposite of *.

The algorithm to compute a maximal disimplicial elimination works in an iterative manner from an input digraph G = [G.sub.1]. At iteration i, the algorithm finds a disimplicial elimination [S.sub.i] of [G.sub.i] by taking any maximal matching of disimplicial arcs of [G.sub.i]. By maximal, we mean that either v [member of] V([S.sub.i]) or w [member of] V([S.sub.i]) for every disimplicial arc v[right arrow]w of [G.sub.i]. Then, the algorithm updates [G.sub.i] into [G.sub.i+1] = [G.sub.i] V([S.sub.i]) for the iteration i + 1. The algorithm stops with output S = [S.sub.1],..., [S.sub.i-1] when [S.sub.i] = [??].

For the sake of notation, in the rest of this section we write [P.sub.i] to denote each parameter P on [G.sub.i] instead of using [P.sub.Gi]; thus, we write [N.sub.i] (v) to denote [??] (v), [[DELTA].sub.i] to denote [??], and so on. When no subscript is wrote, the parameter on G should be understood; e.g., N(v) = [N.sub.G](v), [DELTA] = [[DELTA].sub.G], etc.

The main idea of the algorithm is to compute [S.sub.i], for i > 1, by looking only at the arcs leaving or entering V([S.sub.i-1]). Of all such arcs, we are interested in those with "low degree", which are the analogous of thin arcs for those digraphs that can contain twins (see Proposition 10 below). Let [V.sub.out] = {v [member of] V([G.sub.i]) | v [right arrow] y [member of] E([G.sub.i-1]) for y [member of] V([S.sub.i-1])} and [V.sub.in] = {w [member of] V([G.sub.i]) x [right arrow] w [member of] E([G.sub.i-1]) for x [member of] V([S.sub.i-1])}, i.e., [V.sub.out] and [V.sub.in] are the set of vertices of [G.sub.i] that have an out and in neighbor that was removed from [G.sub.i-1], respectively. For each v [member of] [V.sub.out] (resp. [V.sub.in]), let L(v) be the set of out-neighbors (resp. in-neighbors) of v with minimum in-degree (resp. out-degree) in [G.sub.i]. To compute [S.sub.i], the algorithm first initializes [S.sub.i] := [??] and then it traverses each vertex v [member of] [V.sub.out] [union] [V.sub.in]. For v [member of] [V.sub.out] (resp. v [member of] [V.sub.in]), the algorithm evaluates whether v[right arrow]l (resp. l[right arrow]v) is disimplicial for any l [member of] L(v). If affirmative and L(v) \ V([S.sub.i]) [not equal to] [??], then v [right arrow] w (resp. w [right arrow] v) is inserted into [S.sub.i] for any w [member of] L(v) \ V([S.sub.i]). (Note that w needs not be equal to l; this happens when x[right arrow]l or l[right arrow]x was previously inserted into [S.sub.i] for some x [member of] V([G.sub.i]).) If negative or L(v) [??] V(Si), then v is ignored. By invariant, [S.sub.i] is a matching of [G.sub.i]. Moreover [S.sub.i] contains only disimplicial arcs, as it follows from the following generalization of Lemma 8.

Proposition 10 Let v [member of] [V.sub.out] [union] [V.sub.in] be an endpoint of some disimplicial arc of [G.sub.i]. Then, v[right arrow]w (resp. w[right arrow]v) is a disimplicial arc in [G.sub.i] if and only if w [member of] L(v).

The next proposition shows that, as required, [S.sub.i] is indeed maximal. That is, the algorithm to compute [S.sub.i] is correct.

Proposition 11 If v [right arrow] w is a disimplicial arc of [G.sub.i], then either v [member of] V([S.sub.i]) or w [member of] V([S.sub.i]).

Proof: Observe that v[right arrow]w is not disimplicial in [G.sub.i-1], since otherwise either v or w would have been removed in the update from [G.sub.i-1] to [G.sub.i], by the maximality of [S.sub.i-1]. Hence, there exist x, y [member of] V([G.sub.i-1]) such that y [member of] [N.sup.+](v), x [member of] [N.sup.-](w) and x[right arrow]y [??] E(G). Since v[right arrow]w is disimplicial in [G.sub.i], then either x or y does not belong to [G.sub.i]. In the former case x [member of] V([S.sub.i-1]) and w [member of] [V.sub.in], while in the latter case y [member of] V([S.sub.i-1]) and v [member of] [V.sub.out]. Both cases are analogous, so suppose v [member of] [V.sub.out]. By Proposition 10, w [member of] L(v), while v[right arrow]l is disimplicial for every l [member of] L(v). Consequently, v is ignored by the algorithm (i.e., v [??] V([S.sub.i])) only if w [member of] L(v) [??] V(Si).

Each time an arc v [member of] w is evaluated to be disimplicial, the algorithm works as follows. First, the vertices in [N.sub.i.sup.+](v) [union] [N.sup.-.sub.i](w) are marked, and a variable e is initialized to 0. The purpose of e is to count the number of arcs that leave a vertex in [N.sub.i.sup.-](w) to enter a vertex in [N.sup.-.sub.i] (v). To compute e, each x [member of] [H.sup.-.sub.i](y) is traversed, for every y [member of] [N.sup.+.sub.i](v). If x is marked, then x [member of] [N.sup.-.sub.i](w) and y [member of] [N.sub.i.sup.+](v), thus e is increased by 1; otherwise x [??] [N.sub.i.sup.-](w), thus e remains unchanged. The arc x[right arrow]y is also marked so as to avoid counting it again. When the execution for [N.sub.i.sup.+](v) is done, the algorithm proceeds to traverse each y [member of] [H.sub.i.sup.+](x), for every x [member of] [N.sub.i.sup.-](w), increasing e by 1 when y is marked and x[right arrow]y is not. At the end, all the marks are cleared. Clearly, e counts the number of arcs of [G.sub.i] leaving [N.sub.i.sup.-](w) and entering [N.sub.i.sup.+](v) as each arc x [right arrow] y with x [member of] [N.sub.i.sup.-](w) and y [member of] [N.sub.i.sup.+](v) is traversed at least once. Thus v [member of] wis disimplicial if and only if e = [d.sup.+.sub.i] (v)[d.sup.-.sub.i] (w).

The algorithm implements [G.sub.i] with the h-digraph structure. To compute [S.sub.i], the vertices in V = [V.sub.out] [union] [V.sub.in] need to be traversed; recall that, by definition, [??]. For each traversed v [member of] V, a vertex l [member of] L(v) needs to be located; this costs O([h.sub.i](v)) time if the first vertex given by MinN is taken. Following, v[right arrow]l (or l[right arrow]v) is queried to be disimplicial. For this, the vertices in [N.sup.+.sub.i]+(v) [union] [N.sub.i.sup.-](l) are first marked in O([d.sub.i](v) + [d.sub.i](l)), and then e is computed in O [??] = O([[DELTA].sub.i][[eta].sub.i]) time. Moreover, note that every arc is traversed O(1) times, thus O(min{[m.sub.i], [[DELTA].sub.i][[eta].sub.i]}) in actually spent to check if v[right arrow]l is disimplicial. When v[right arrow]l (or l[right arrow]v) is disimplicial, MinN is invoked to obtain L(v), which is then traversed so as to locate the arc v[right arrow]w (or w[right arrow]v) to be inserted into [S.sub.i]. Note that every vertex z [member of] L(v) that is traversed while looking for w belongs to V([S.sub.i]) at the end of step i. Also, z will be evaluated no more than O([d.sub.i](z)) times, once for each v [member of] [N.sub.i](z) such that L(v) is considered. Thus, all the required traversals to the sets {L(v) | v [member of] V} consume O [??] time. Summing up, the time required to compute [S.sub.i] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Before the algorithm starts, [G.sub.1] is initialized with an invocation to Initialize at the cost of O([alpha]m) time. Similarly, after each iteration, V([S.sub.i]) is removed from [G.sub.i] using the operation Remove. Note that each vertex is removed exactly once, hence O([alpha]m) time is totally consumed. Let k be the number of iterations required by the algorithm and S be the output disimplicial elimination. Since [S.sub.1] can be computed in O([alpha]m) time and [??] [S.sub.i] = S is a matching, we obtain that the total time required by the algorithm is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Since the h-digraph structure uses O(m) bits, the space complexity is linear.

4.2 Disimplicial M -eliminations

We now consider the restricted problem of finding a maximal disimplicial M-elimination of a digraph G, when an input matching M is given. A disimplicial M-elimination is just a disimplicial elimination S of G included in M; S is maximal when no arc of M \ S is disimplicial in G \ V(S).

This time, the idea is to take advantage of the relation between disimplicial arcs and transitive vertices. Say that a sequence [v.sub.1],..., [v.sub.k] is a transitive V-elimination of a digraph D, for V [??] V(D), when [v.sub.i] is transitive in D \ {[v.sub.1],..., [v.sub.i-1]}, for every 1 [less than or equal to] i [less than or equal to] k. Suppose S = [v.sub.1][right arrow][w.sub.1],..., [v.sub.k][right arrow][w.sub.k] [??] M and let [G.sub.1] = G and [M.sub.1] = M. For 1 [less than or equal to] i [less than or equal to] k, define

* [G.sub.i+1] = G \ {[v.sub.i], [w.sub.i]},

* [M.sub.i+1] = [M.sub.i] \ {[v.sub.i][right arrow][w.sub.i]},

* [V.sub.i] = {(v, w) v [right arrow] w [member of] [M.sub.i]}, and

* [T.sub.i] = ([v.sub.1], [w.sub.1]),...,([v.sub.i], [w.sub.i]).

By definition, [D.sub.i] has a vertex (v, w) for each v[right arrow]w [member of] [M.sub.i] and a vertex (v, v) for each v [member of] [G.sub.i] \ V([M.sub.i]) where (v, w)[right arrow](x, y) is an arc of [D.sub.i] if and only if v[right arrow]y [member of] E(G). It is not hard to see, then, that [D.sub.i+1] = Join([G.sub.i+1], [M.sub.i+1]) = Join([G.sub.i], [M.sub.i]) \ {([v.sub.i], [w.sub.i])} = [D.sub.i]{([v.sub.i],[w.sub.i])}. Moreover, by Theorem 6, ([v.sub.i], [w.sub.i]) is transitive in [D.sub.i] if and only if [v.sub.i][right arrow][w.sub.i] is disimplicial in [G.sub.i]. Hence, by induction, S = [S.sub.k] is a disimplicial M-elimination of G if and only if [T.sub.k] is a transitive V-elimination of D for V = [V.sub.1] and D = [D.sub.1]. Moreover, S is maximal if and only if [T.sub.k] is maximal. This discussion is summarized in the following theorem.

Theorem 12 Let M be a matching of a digraph G, S = [v.sub.1][right arrow][w.sub.1],..., [v.sub.k] [right arrow] [w.sub.k] be a sequence of arcs of G, D = Join(G, M), and T = ([v.sub.1], [w.sub.1]),..., ([v.sub.k], [w.sub.k]). Then, S is a maximal disimplicial M-elimination of G if and only ifT is a maximal transitive V -elimination of D.

In view of Theorem 12, we discuss how to obtain a maximal transitive V-elimination of a digraph [D.sub.1] = D. The algorithm works in an iterative manner from [D.sub.1] = D. At each step i, a transitive vertex [v.sub.i] [member of] V is removed from [D.sub.i] so as to obtain [D.sub.i+1]; if no such vertex exists, then the algorithm halts with output [v.sub.1],..., [v.sub.i-1]. To be able to find [v.sub.i] efficiently, the following data is maintained by the algorithm prior to the execution of iteration i:

* [D.sub.i], implemented with the h-digraph structure,

* the set of transitive vertices [T.sub.i] of [D.sub.i],

* the number [t.sub.i](v) of arcs leaving [N.sup.-] (v) and entering [N.sup.+] (v) in [D.sub.i], for v [member of] V([D.sub.i]).

With the above information, any vertex of [T.sub.i] is taken by the algorithm to play the role of [v.sub.i]. Once [v.sub.i] is selected, the algorithm has to update its data structure for the next iteration. The update of [D.sub.i] into [D.sub.i+1] = [D.sub.i]\ {[v.sub.i]} is handled by the Remove operation of the h-digraph structure. The update of [t.sub.i] into [t.sub.i+1] is done in two phases. The first phase decrements [t.sub.i](w) by 1 for each arc z [right arrow] w such that w, z [member of] [N.sup.-] (v), while the second phase decrements [t.sub.i](w) by 1 for each arc w [right arrow]z such that w, z [member of] [N.sup.+] (v). The N' operation of the h-digraph structure is employed for this step. Finally, observe that w [member of] [T.sub.i+1] if and only if either w [member of] [T.sub.i] or w [member of] N([v.sub.i]) and [t.sub.i+1](w) = [d.sup.-](w)[d.sup.+](w). Thus, the update of [T.sub.i] into [T.sub.i+1] takes Before the first step can take place, [D.sub.1] is initialized with an invocation to Initialize. Note that Remove and N' are called O(1) times for each vertex of D, thus O([alpha]m) total time is consumed by the algorithm. As for the space, [D.sub.i] requires O(m) space while the remaining variables consume O(n) bits.

Since D = Join(G, M) can be computed in linear time, [[alpha].sub.G] = [THETA]([[alpha].sub.D]), and [m.sub.G] = [THETA]([m.sub.D]) we conclude that a maximal disimplicial M-elimination can be computed in O([[alpha].sub.G][m.sub.G]) time and linear space.

5 Reduced dicliques

By definition, a reflexive vertex v is transitive if and only if v [right arrow] v is a disimplicial arc. Hence, if D is an order graph, then E(D) can be partitioned into a family of dicliques, all of which are reduced. Moreover, by Proposition 1, G = Split(D) is an ST graph and E(G) can also be partitioned into a family of dicliques, all of which are reduced. The purpose of this section is to study two graph classes that admit this kind of partition.

5.1 Weakly diclique irreducible digraphs

Say that a digraph is weakly diclique irreducible (WDI) when all its arcs belong to a reduced diclique. By Propositions 1 and 7, G is WDI if and only both Split(G) and Repr(G) are WDI; for this reason, we consider only ST graphs with no twins for this section. The next theorem, combined with Lemma 4, shows that there is a one-to-one correspondence between the class of twin-free ST graphs that admit a perfect matching of disimplicial arcs and the class of order graphs. A direct consequence of this theorem is that the recognition of WDI digraphs is at least as hard as the recognition of order graphs.

Theorem 13 A reflexive oriented graph D is transitive if and only if G = Split(D) is WDI. Furthermore, if G is WDI, then the perfect matching M = {out(v) [right arrow] in (v) | v [member of] V(D)} is the set of disimplicial arcs of G.

Proof: If D is a reflexive oriented graph, then (i) M is a perfect matching of G, and (ii) out(v) [right arrow] in(w) and out(w) [right arrow] in (v) are both arcs of G if only if v = w. Then, out(v) [right arrow] in(v) belongs to a reduced diclique if and only if it is disimplicial. Since every arc out(v) [right arrow] in(w) [member of] E(G) belongs to the diclique {out(v)} [right arrow] {in(v), in(w)} of G, we conclude that G is WDI if and only if all the arcs of M are disimplicial. Therefore, by Theorem 3, G is WDI if and only if D is transitive. Moreover, since G is twin-free by (ii), and the set of thin arcs is a matching containing M by Lemma 8, we conclude that no arc of E(G) \ M is disimplicial.

The following theorem shows that the recognition of WDI digraphs is not harder than the problem of listing the acyclic triangles of a digraph; a, b, c [member of] V(D) is an acyclic triangle when a[right arrow]b, a[right arrow]c, c[right arrow]b are arcs of D. All such triangles can be found in either O([alpha]m) time and O(m) space or O([n.sup.[omega]]) time and [THETA]([n.sup.2]) space [3]. We conclude then that, unless it is proved that recognizing order graphs is strictly easier than listing triangles, the recognition of WDI digraphs is well solved.

Theorem 14 An ST graph G with no twins is WDI if and only if:

* D = Join(G) is transitive, and

* for every arc a[right arrow]b of D there exists a vertex c of D such that a [right arrow] c and c [right arrow] b are also arcs of D.

Proof: Suppose G is WDI. By definition, every vertex (v, w) of D that is neither a source nor a sink corresponds to a thin arc v[right arrow]w of G. Since G is WDI, we know that v[right arrow]w belongs to a diclique N(y)[right arrow]N(x) for some disimplicial arc x[right arrow]y, thus N(x) [??] N(v) and N(y) [??] N(w). Moreover, taking into account that v[right arrow]wis thin, it follows that d(v) [less than or equal to] d(x) and d(w) [less than or equal to] d(y), thus N(v) = N(x) and N(w) = N(y). Therefore, v[right arrow]w is disimplicial in G and, by Theorem 6, (v, w) is transitive in D; in other words D is transitive. Now, consider any arc (v, v')[right arrow](w', w) of D. By definition, v[right arrow]w is an arc of G that belongs to some reduced diclique N(y)[right arrow]N(x). By Lemma 8, x[right arrow]y is a thin arc and, since v[right arrow]y and x[right arrow]w are arcs, it follows that (v, v')[right arrow](x, y) and (x, y)[right arrow](w', w) are arcs of D.

For the converse, let v[right arrow]w be any arc of G and (v, v ')and(w',w)bethe vertices of D that correspond to v and w (possibly v = w'). By definition, (v, v')[right arrow](w, w') is an arc of D, thus, there exists a vertex (x, y) of H such that (v, v') [right arrow] (x, y) and (x, y) [right arrow] (w, w) are arcs of D (possibly v = x or y = w). Since (x, y) is neither a source nor a sink of D, then it follows that (x, y) is transitive in D and x [right arrow] y is a thin arc of G. So, by Theorem 6, x[right arrow]y is a disimplicial arc of G which means that N(y)[right arrow]N(x) is a reduced diclique. Now, taking into account that (v, v')[right arrow](x, y) and (x, y)[right arrow](w', w) are arcs of D, it follows that v [member of] N(y) and w [member of] N(x), i.e., v[right arrow]w belongs to a reduced diclique. In other words, G is WDI.

5.2 Diclique irreducible digraphs

In the remaining of this section we work with a subclass of WDI digraphs, namely the diclique irreducible digraphs. A digraph G is diclique irreducible (DI) when all its maximal dicliques are reduced. Note that G is WDI; indeed, every arc of G belongs some diclique which must be reduced by definition. Again, G is DI if and only if both Split(G) and Repr(G) are DI, thus we restrict our attention to ST graphs with no twins. By Theorem 14, we know that Join(G) is a transitive oriented graph; the following lemma proves that Join(G) must also be reflexive.

Lemma 15 If an ST graph with no twins is DI, then its set of thin arcs is a perfect matching.

Proof: Let G be an ST graph that is DI and has no twins, v be a source vertex of G, and d(w) be minimum among the neighbors of v. Since G is DI, it follows that v[right arrow]w belongs to some diclique N(y)[right arrow]N(x) for a disimplicial arc x[right arrow]y. Then N(w) = N(y) which implies that w = y as G is twin-free. Consequently, the thin neighbor of v is [theta](v) = w. Moreover, as x[right arrow]y = w is disimplicial, it follows that the thin neighbor of w is [theta](w) = x. Suppose, to obtain a contradiction, that x [not equal to] v. Then, since d(x) < d(v), we conclude that there exists z [member of] N(v) N(x). Thus, {v}[right arrow]{w, z} is a diclique that must be contained in B = N(b)[right arrow]N(a) for some disimplicial arc a right arrow b. The same arguments used before allow us to conclude that w = b = [theta](v) and [theta](w) = a. This is clearly a contradiction because x = [theta](w) does not belong to Bas it is not adjacent to z. We conclude, therefore, that v = [theta](w) = [theta]([theta](v)). Analogously, w = [theta]([theta](w)) for every sink vertex w, thus every vertex belongs to a thin arc. That is, the set of thin arcs is a perfect matching of G.

Corollary 16 If an ST graph with no twins is DI, then Join(G) is an order graph.

Recall that order graphs are the graph theoretical equivalents of finite posets. When G is DI, the poset defined by Join(G) turns out to be what in order theory is known under the name of dedekind complete. We do not define what a dedekind complete poset is; in turn, we translate this concept in graph theoretic terms.

Let D be a digraph. Say that u [member of] V(D) (resp. l [member of] V(D)) is an upper bound (resp. a lower bound) of V [??] V(D) when v[right arrow]u [member of] E(D) (resp. l [right arrow]v [member of] E(D)) for every v [member of] V. We write [mu](V) and [lambda](V) to denote the sets of upper and lower bounds of V, respectively. When [mu](V) (resp. [lambda](V)) is nonempty, the set V is said to be bounded from above (resp. below). Every lower bound of [mu](V) that belongs to [mu](V) is a supremum of V, while every upper bound of [lambda](V) that belongs to [lambda](V) is an infimum of V. Note that V has at most one supremum (resp. infimum) when D is an oriented graph. A dedekind graph is an order graph D such that every [??] c V [??] V(D) that is bounded from above has a supremum. It is well known that an order graph D is dedekind if and only if every [??] c V [??] V(D) that is bounded from below has an infimum.

The reason why dedekind graphs come into play in the characterization of DI digraphs has to do with the way Join(G) encodes the dicliques and disimplicial arcs of G. Roughly speaking, a disimplicial arc v[right arrow]w of G is a transitive vertex (v, w) of Join(G) where N(v) and N(w) corresponds to the lower and upper bounds L, U of {(v, w)}, respectively. Moreover, (v, w) is both the infimum and supremum of U and L, respectively. This somehow explains why dedekind graphs appear when every diclique has a disimplicial arc. The complete proof is given in the next theorem.

Lemma 17 Let G be a digraph, V, W be nonempty subsets of V(G), D = Join(G), and L = {(v, v') [member of] V(D) |v [member of] V} and U = {(w',w) [member of] V(D) | w [member of] W}. Then, V [right arrow] W is a diclique of G if and only if L [??] [lambda](U) and U [??] [mu](L). Furthermore, V [right arrow] W is a maximal diclique exactly when L = [lambda](U) and U = [mu](L).

Proof: Just observe that, by definition, v[right arrow]w [member of] E(G) for every v [member of] V and w [member of] W if and only if (v, v') [right arrow] (w', w) [member of] E(D) for every (v, v') [member of] L and (w', w) [member of] U. That is, V [right arrow] W is a diclique of G if and only if L [??] [lambda](U) and U [??] [mu](L). Moreover, using the same argument, the maximality of V[right arrow]W occurs precisely when L = [lambda](U) and U = [mu](L).

Theorem 18 Let G be an ST graph with no twins. Then G is DI if and only if Join(G) is dedekind.

Proof: Suppose G is DI, let D = Join(G), and consider any nonempty M [??] V(D) bounded from above. Let (a) U = [mu](M) and (b) L = [lambda](U), and observe that (c) U = [mu](L). By definition, L = {(v,v') [member of] V(D) | v [member of] V} and U = {(w',w) [member of] V(D) | w [member of] W} for some V, W [??] V(G). By Lemma 17, V [right arrow] W is a maximal diclique of G, thus it contains some disimplicial arc v[right arrow]w. By Lemma 8, v [right arrow] w is a thin arc, thus (v, w) is a vertex of D. Moreover, (v,w) [member of] L[intersection]U because v [member of] V and w [member of] W. Then, by (b) and (c), it follows that (v, w) is the supremum of L and the infimum of U, while by (a), (v, w) is a supremum of M as well.

For the converse, suppose V[right arrow]W is a maximal diclique of G and let (a) L = {(v, v') G V(D) | v [member of] V} and (b) U = {(w', w) [member of] V(D) | w [member of] W}. By Lemma 17, L = [lambda](U) and U = [mu](L), hence, since D is dedekind, it follows that L [intersection] U contains some vertex (v, w) such that (c) L = [N.sup.-]((v, w)) and (d) U = [N.sup.+]((v, w)). By (a) and (c), and considering how Join works, we conclude that V = [N.sub.G](w), while W = [N.sub.G](v) by (a) and (d). In other words, v[right arrow]w is a disimplicial arc of V[right arrow]W.

Corollary 19 A digraph D is dedekind if and only if Split(D) is DI.

Proof: By Lemma 4, D = Join(G) for G = Split(D), while, by Theorem 18, D is dedekind if and only if G is DI.

By Theorem 18 and Corollary 19, DI and dedekind graphs are equally hard to recognize, and the recognition can be done in polynomial time rather easily. Just observe that a DI digraph has at most m maximal dicliques, one for each disimplicial arc. Then, a recognition algorithm needs to traverse at most m + 1 maximal dicliques before finding one that is not reduced. To test if a diclique is reduced, it is enough to check that it contains a precomputed disimplicial arc. Since the disimplicial arcs can be found in O([alpha]m) time, and the m + 1 dicliques of can be traversed in O([nm.sup.2]) time [4], an O([nm.sup.2]) time algorithm is obtained. We now describe an O(nm) time and O(m) space algorithm that exploits the definition of dedekind graphs. The following simple lemma is the key of the algorithm.

Lemma 20 An order graph D is dedekind if and only if for every v, w [member of] V(G) with [mu]({v, w}) [not equal to] [??] there exists u [member of] V(G) such that |[mu]({v,w})| = [d.sup.+](u).

Proof: Suppose D is dedekind and let u be the supremum of {v, w}, for {v, w} [??] V(D) bounded from above. By definition, v[right arrow]u [member of] E(D) and w[right arrow]u [member of] E(D), thus [N.sup.+](u) [??] [mu]({V,w}) because u is transitive. Also by definition, u[right arrow]z [member of] E(D) for every z [member of] [mu]({v,w}), thus [mu]({v,w}) [??] [N.sup.+](u). Therefore, |[mu]({v,w})| = |[N.sup.+](u)| = [d.sup.+](u).

For the converse, observe again that [N.sup.+](u) [??] [mu]({v,w}) for every u [member of] [mu]({v,w}), because u is transitive. So, if u [member of] [mu]({v,w}) has degree |[mu]({v,w})|, then [N.sup.+](u) = [mu]({v,w}), which means that {v, w} has a supremum. That is, {v, w} has a supremum for every {v, w} [??] V(D) bounded from above. It is well known (taking into account that dedekind graphs correspond to dedekind complete finite posets) that, in this case, D is dedekind.

The algorithm to determine if an order digraph D is dedekind traverses [mu]({v, w}), for each pair of vertices v, w [member of] V(G), searching for a vertex u with [d.sup.+](u) = |[mu](v,w)|. For the implementation, an outer loop traverses each v [member of] V(G) and an inner loop traverses each w [member of] V(G) \ {v}. Before the inner loop begins, all the vertices in [N.sup.+](v) are marked in O(d(v)) time. Then, in the inner loop, [mu]({v, w}) is obtained in O(d(w)) time by filtering those vertices of [N.sup.+](w) that are marked. The degree of all the vertices in [mu]({v, w}) is the evaluated in O(d(w)) time as well. The total time required by the algorithm

[FIGURE 3 OMITTED]

is, therefore,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

while the space complexity is O(m) bits. Since order graphs can be recognized in O([alpha]m) time and O(m) space, we conclude that the recognition DI and dedekind graphs takes O(nm) time and O(m) space.

6 Results on bipartite graphs and further remarks

A bipartite graph is a triple G = (V,W,E) where an unordered pair vw belongs to E only if v [member of] V and w [member of] W. An edge vw is bisimplicial when every vertex in N(v) is adjacent to all the vertices in N(w). By replacing each vw by an arc v [right arrow] w, an ST graph [??] is obtained. Moreover, an edge vw of G is bisimplicial precisely when v [right arrow] w is disimplicial in [??]. So, the algorithms in this article can be applied directly to bipartite graphs so as to solve the corresponding problems. In this section we summarize the results for bipartite graphs while we provide further remarks.

In Section 3 we proved that listing the bisimplicial edges of a bipartite graph and finding the transitive vertices of a digraph are equally hard problems. The good news is that the bisimplicial edges of a bipartite graph can be found in O([alpha]m) time, improving over the previous O(nm) time algorithm; the bad news is that we cannot improve this algorithm further using only O(m) space, unless an o([alpha]m) time algorithm to find the transitive vertices of a digraph is provided.

In Section 4 we describe an O(min{[DELTA][eta], m}m) time and O(m) space algorithm to compute a maximal disimplicial elimination of [??]. When applied to bipartite graphs, a maximal elimination scheme S is obtained. Since [eta] < [DELTA], our algorithm improves the worst-case time bound of [1] for all the bipartite graphs with [DELTA] = o([??]). Golumbic and Goss [6] proved that S is perfect whenever G admits a perfect elimination scheme, thus the algorithm can be used to recognize if a sparse graph is perfect elimination bipartite. The concept of perfect elimination graphs can be generalized to digraphs and disimplicial eliminations. Just say that a digraph D is perfect disimplicial elimination whenever it admits a disimplicial elimination S such that G \ V(S) has no arcs. Unfortunately, finding a maximal disimplicial elimination is not enough to determine if D is perfect, as it is shown in Figure 3. So, the recognition of perfect disimplicial elimination remains open.

In Section 4 we also consider the problem of computing a maximal disimplicial M-elimination, for an input matching M, for which we provide an O([alpha]m) time and O(m) space algorithm. Rose and Tarjan [10] proved that this problem is at least as hard as determining if a given digraph is transitive. Up to this date, the best algorithm to determine if a sparse graph is transitive costs O([alpha]m) time and O(m) space. So, the problem is well solved, without using more than O(m) space, unless better algorithms for recognizing transitive digraphs are found.

Recall one of the motivations for finding a maximal disimplicial elimination is to be able to perform some iterations of the Gaussian elimination process on a sparse matrix M with the guaranty that no zero entry will change into a non-zero value. Being M sparse, we expect [[alpha].sub.G] [approximately equal to] 1 and [[DELTA].sub.G] [approximately equal to] 1 for G = G(M). If so, then finding the disimplicial elimination and applying the corresponding iterations of the Gaussian elimination require linear time. That is, our algorithm can be used to preprocess M, say before solving the system Mx = b. In the worst case no zero fill-in entry is found and thus M remains the same. Yet, the extra time paid for this examination is low.

In Section 5 we deal with the classes of WDI and DI digraphs. We noted that every order graph D is uniquely associated with a twin-free ST graph G that is WDI, namely G = Split(D). In fact, each v [member of] V(D) gets transformed into the disimplicial arc out(v)[right arrow]in(v) of G, thus G has a perfect matching of disimplicial arcs. The converse is also true, any ST graph that has a perfect matching of disimplicial arcs must be isomorphic to Split(D) for some order graph D. We remark that the order relation[right arrow]of D is somehow preserved in G. Indeed, note that v[right arrow]w [member of] E(D) only if w[right arrow]v [??] E(D), thus out(v) [right arrow] in(w) [member of] E(G) while out(w) [right arrow] in(v) [??] E(G). Hence, by transitivity, v [right arrow] w [member of] E(D) if and only if N(out(v)) [subset] N(out(w)) and N(in(w)) [subset] N(in(v)). In this section we also proved that G is also DI whenever D is a dedekind graph. Moreover, each A [??] V(D) with supremum u is associated with a reduced biclique V[right arrow]W such that V = {out(v) | v[right arrow]u [member of] E(D)} and W = {in(w) | u[right arrow]w [member of] E(D)}. Note that, in particular, out(u)[right arrow]in(u) is the disimplicial arc of V[right arrow]W.

References

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(*) Supported by ANPCyT grant PICT-2013-2205 and PUNQ grant 1451/15.

Martiniano Eguia (1)

Francisco J. Soulignac (2*)

(1) Departamento de Computacion, FCEN, Universidad de Buenos Aires, Argentina

(2) CONICET and Departamento de Ciencia y Tecnologia, Universidad Nacional de Quilmes, Argentina received 27th July 2014, revised 13th May 2015, accepted 1st Sep. 2015.

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Author: | Eguia, Martiniano; Soulignac, Francisco J. |
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Publication: | DMTCS Proceedings |

Article Type: | Report |

Geographic Code: | 1USA |

Date: | Jul 1, 2015 |

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