Univ Lyon, CNRS, ENS de Lyon, Université Claude Bernard Lyon 1, LIP UMR5668, [email protected]://orcid.org/0000-0002-1653-5822 \CopyrightÉdouard Bonnet\ccsdesc[100]Theory of computation → Graph algorithms analysis\supplement\hideLIPIcs\EventEditorsJohn Q. Open and Joan R. Access \EventNoEds2 \EventLongTitle42nd Conference on Very Important Topics (CVIT 2016) \EventShortTitleCVIT 2016 \EventAcronymCVIT \EventYear2016 \EventDateDecember 24–27, 2016 \EventLocationLittle Whinging, United Kingdom \EventLogo \SeriesVolume42 \ArticleNo23
4 vs 7 sparse undirected unweighted Diameter is SETH-hard at time
Abstract
We show, assuming the Strong Exponential Time Hypothesis, that for every , approximating undirected unweighted Diameter on -vertex -edge graphs within ratio requires time, even when . This is the first result that conditionally rules out a near-linear time -approximation for undirected Diameter.
keywords:
Diameter, inapproximability, SETH lower bounds, k-Orthogonal Vectorscategory:
1 Introduction
The diameter of a graph is the length of a longest shortest path between two of its vertices. We write Diameter for the algorithmic task of computing the diameter of an input graph. Throughout the paper, implicitly denotes the number of vertices of a graph, and , its number of edges. We will often prefix Diameter with undirected/directed to indicate whether or not edges may be oriented111In directed Diameter, we are to compute the length of a longest shortest path taken from any vertex to any vertex., and unweighted/weighted to indicate whether or not non-negative edge weights are allowed.
A fairly recent and active line of work aims to determine the best runtime for an algorithm approximating Diameter within a given ratio. First, there is an exact algorithm running in time222where suppresses the polylogarithmic factors , which computes shortest-path trees from every vertex of the graph. Secondly, there is a -approximation running in time , which computes a shortest-path tree from an arbitrary vertex and outputs the largest distance found. There are an time -approximation for directed weighted Diameter [1, 15, 6], and for every non-negative integer , an time -approximation333with an extra additive factor depending on the weights for undirected weighted Diameter [4]. We refer the interested reader to the survey of Rubinstein and Vassilevska Williams [16].
We will now focus on sparse graphs, for which . This is because the current paper deals with conditional lower bounds on approximating Diameter, and all such results even work with that restriction. Observe that, on sparse graphs, the first result of the previous paragraph is a near-quadratic 1-approximation, while the second result is a near-linear 2-approximation. One can represent these ratio-runtime trade-offs in the two-dimensional plane. The ultimate goal of fine-grained complexity, in that particular context, is to obtain a complete curve of algorithms linking these two extreme points, matched by tight conditional lower bounds. We now present one way of deriving conditional lower bounds for polytime problems.
Lower bounds based on the Strong Exponential Time Hypothesis
The Strong Exponential Time Hypothesis (SETH, for short) asserts that for every , there is an integer such that -SAT cannot be solved in time on -variable instances [11]. At first glance, this assumption should only be useful to rule out some specific running time for NP-hard problems which, like the satisfiability problem, seems to require superpolynomial time. Such conditional lower bounds to classical [7] or parameterized algorithms [8] are overviewed in a survey [14] on the consequences of the SETH (as well as the weaker assumption ETH) on solving computationally hard problems.
Interestingly, using the SETH to rule out a given running time for a polynomial-time solvable problem took more time. In a survey of fine-grained complexity [18], Vassilevska Williams dates the first reduction (albeit used positively) from SAT to a problem in P back to 2005 [17]. We will see that this reduction to 2-Orthogonal Vectors, where one wants to find two orthogonal -vectors within a given list, is very relevant to the fine-grained complexity of Diameter. As it turns out, the first SETH-based lower bound for a polytime graph problem occurred almost a decade later, on the very unweighted undirected Diameter [15].
There might have been a psychological barrier in reducing a “hard” problem to an “easy” one, in order to derive a conditional lower bound. However this makes perfect sense. Let us give an apropos example. Suppose (as it is actually the case) that one can create in time a list of 0,1-vectors with , from an -variable SAT formula, such that there is pair of orthogonal vectors in the list if and only if the formula is satisfiable. Now a truly subquadratic algorithm, that is in time for some , for 2-Orthogonal Vectors would enable to solve SAT in time for some , contradicting the SETH. We thus say that 2-Orthogonal Vectors is SETH-hard at time , and more generally a problem is SETH-hard at time if it requires time under the SETH.
SETH lower bounds for Diameter
There is a handful of SETH-hardness results on approximating Diameter [15, 2, 3, 9, 12, 13]. Unless the SETH fails, any -approximation for sparse undirected unweighted Diameter, with , requires time [15] (this is the above-mentioned seminal result to the fine-grained complexity within P), whereas any -approximation requires time [12] (an early version of [13]). Since a -approximation of Diameter running in near-linear time was consistent with the then knowledge (up until mid-August 2020, even in weighted directed graphs) Rubinstein and Vassilevska Williams [16] and Li [12] ask for such an algorithm or some lower bounds with a ratio closer to 2.
In the last few months, there were several developments on directed graphs. The author showed that, under the SETH, -approximating sparse directed weighted Diameter requires time [3]. Then Wein and Dalirrooyfard [9], and independently, Li [13] (an updated version of [12]) both show that not only this result holds on directed unweighted graphs but they generalize it in the following way: Unless the SETH fails, for every and every integer , -approximating directed unweighted Diameter requires time .
Despite these advances, a near-linear time -approximation for the undirected Diameter may still have existed. In this paper, we rule out this possibility by showing the following (see Figure 1 for a visual summary of what is now known on approximating undirected Diameter).
Theorem 1.1.
Unless the SETH fails, for any , -approximating Diameter on undirected unweighted -vertex -edge graphs requires time.
In particular we resolve [16, Open Question 2.2.], on the existence of a near-linear time -approximation for undirected Diameter, by the negative.
In light of the recent results (see in particular the paragraph on barriers to SETH-hardness), it is reasonable to conjecture that the four variants of sparse Diameter (undirected/directed unweighted/weighted) are equally approximable. More precisely, we venture the following optimistic prediction.
Conjecture 1.2.
Sparse (un)directed (un)weighted Diameter is -approximable in time for every . Unless the SETH fails, approximating sparse (un)directed (un)weighted Diameter within ratio better than requires time for every .
1.2 is naturally equivalent to obtaining the algorithms for the directed weighted Diameter and the SETH-hardness for the undirected unweighted Diameter. Settling the conjecture would give a complete landscape of the approximability of Diameter, where if one represents the results in the two-dimensional space of approximation factor vs runtime exponent, the feasible and infeasible regions are separated by a rectilinear curve with infinitely many corners (the black curve drawn in Figure 1). In that respect, our contribution is to give the third lower bound on the curve (i.e., North-East corner) after Roditty and Vassilevska Williams gave the first [15], and Li, the second [12]. Hopefully our new ideas (together with the recent constructions in the directed case of Wein and Dalirrooyfard [9], and Li [13]) will also help in generalizing the lower bound predicted by 1.2 to every positive integer .
Barriers to SETH lower bounds
1.2 is partly prompted by intriguing results due to Li [13]. To state them, we need to recall the definition of a strengthening of SETH introduced by Carmosino et al. [5]. It is called NSETH for Nondeterministic SETH. NSETH asserts that for every , there is an integer such that the -Taut problem cannot be solved in non-deterministic time , where -Taut asks, given a -DNF formula whether every truth assignment satisfies it (in other words, if it is a tautology). Li shows, for all four variants of Diameter but the directed weighted one, that no point positioned strictly above the rectilinear black curve of Figure 1 can be shown SETH-hard, under the NSETH (and, if randomized reductions are permitted, under a stronger assumption, called NUNSETH for Non-Uniform NSETH).
1.2 is very optimistic since it predicts that every such point will be explained by an algorithm. There are many alternatives to that event. For instance NSETH could be false444If we are totally honest, even the weaker SETH does not gather such a wide consensus, and is false if quantum computation is allowed., or the intractability region could extend further North via a non SETH-based reduction, or via a deterministic SETH-based reduction in the directed weighted case. Besides it would require significant progress in approximating the sparse directed Diameter, when currently no algorithm running in time achieves approximation factor better than 2.
Techniques
Like every mentioned Diameter lower bound (for more details, see the paragraphs on -OV and Diameter in the surveys [18, 16]), we reduce from -Orthogonal Vectors, where one seeks, in a given set of -vectors of dimension , vectors such that at every index, at least one of these vectors has a 0 entry. Under the SETH, -Orthogonal Vectors requires time [17], even when is polylogarithmic in .
Here we will reduce from 4-Orthogonal Vectors. We thus wish to build a graph on vertices and edges with diameter 7 if there is an orthogonal quadruple (i.e., a solution to the the 4-Orthogonal Vectors instance), and diameter 4 otherwise. Following a reduction to -Diameter555where one seeks the length of a longest shortest path from a vertex of to a vertex of by Backurs et al. [2] (arguably also following [15]) most of the reductions (as in [3, 9, 13]) feature layers , with only (forward) edges between two consecutive . The vertices within the same layer share the same number of “vector attributes” and “index attributes”. The interplay between vector and index attributes in defining the vertices and edges is made so that if there are no orthogonal vectors, then there are paths of “optimal” length between every pair in , whereas if there is set of orthogonal vectors, a pair in jointly encoding is suddenly very far apart (usually and ideally at distance ).
Then the challenge is to make sure that, on NO-instances, the other pairs (not in ) are at distance at most , without destroying the previous property. The core of our reduction is similar to our previous construction for directed weighted Diameter [3]. However we simplify and streamline it in the following way. As in the first construction of Li [12], we collapse some layers into one. We will have and , while is called . This makes the case analyses simpler (fewer kinds of pairs to consider).
At this point, we face the same issue as in [3]: There are pairs in that are too far apart. On directed graphs, this can be fixed by adding parallel layers and appropriate “back” edges [3, 9] or simply “back” edges [13]. This is no longer an option. Instead we add a set of vertices with only index attributes. These vertices link the right pairs of with path of length 4 (we are back to the first variation on the theme [15]). To emphasize that the situation is somewhat delicate, we observe that not all the pairs of can be at distance 4, since otherwise every pair in is at distance at most 6. We set at distance 3 of (by initially putting edges of weight 3). This permits to cliquify without creating -paths of length at most 6. In turn, this puts every pair involving at distance at most 4, as well as pairs of . Note that as long as (or ), one can have all the pairs of at distance 4 (or ), without creating undesired -paths of length at most 6 (or ).
We then remove the weight-3 edges between and . This involves some vertex splits transforming into , and a simpler echo of the idea of having the clique , with a clique connecting appropriately the pairs in .
Organization
In Section 2, we recall graph-theoretic notations, and give the relevant background on the Orthogonal Vectors problem. In Section 3, we present a simpler reduction with edge weights. It thus achieves the statement of Theorem 1.1 for sparse undirected weighted Diameter. In Section 4, we tune this reduction to get rid of the edge weights, and establish Theorem 1.1.
2 Preliminaries
We use standard graph-theoretic notations. If is a graph, denotes its vertex set, and , its edge set. We denote the edge set between and by . If , denotes the subgraph of induced by . Weighted graphs have positive edge weights. (Throughout the paper, we will only need edges of weight 1 and 3.) We exclusively deal with undirected graphs (for which the distance function is symmetric). For , denotes the distance between and in , that is, the number of edges in a shortest path between and . For every positive integer and every vertex , denotes the set of vertices such that . In unweighted graphs, the closed neighborhood of , denoted , coincides with . However in a weighted graph would for instance not contain the neighbors of via an edge of weight greater than 1. This subtlety will arise only once, and we will remind the reader in due time. For every positive integer and every vertex , denotes the set of vertices such that for some . We observe that, in unweighted graphs, coincides with where is applied times and is the closed neighborhood of . We drop the subscript in the above notations, if the graph is clear from the context.
We denote by the diameter of , that is, . The Diameter problem asks, given a graph , for the value of . We call -path, a path going from vertex to vertex , and -path (with possibly ), any path going from some vertex to some vertex .
If is a positive integer, denotes the set . If is a vector and is a positive integer, then denotes the -th coordinate of . We use to denote the value with the largest number of occurrences in the tuple .
For every fixed positive integer , the -Orthogonal Vectors (-OV for short) problem is as follows. It asks, given a set of 0,1-vectors in , if there are vectors such that for every , , or equivalently, does not hold. Williams [17] showed that, assuming the SETH, -OV requires time with . Furthermore, using the Sparsification Lemma [10], this lower bound holds even when, say, . Here we will leverage this lower bound for . This is, in the context of the SETH-hardness of approximating Diameter, a usual opening step: For example, Roditty and Vassilevska Williams [15] uses this lower bound for , Li [12], for and general , the author [3], for , Wein and Dalirrooyfard [9], for general and .
3 A simpler reduction with edge weights
From any set of vectors in , we build an undirected weighted graph (with edge weights 1 and 3, only) with vertices and edges such that if admits an orthogonal quadruple then the diameter of is (at least) 7, whereas if has no orthogonal quadruple then the diameter of is (at most) 4. We recall that 4-OV requires time, unless the SETH fails, even when [17]. In that case, the graph has vertices and edges. Hence any algorithm approximating sparse undirected weighted Diameter within ratio better than in time , with , would refute the SETH.
3.1 Construction
We first describe the vertex set of , then its edge set, and finally check that the number of vertices and edges are as announced.
Vertex set
Every vertex of is the concatenation of a possibly empty tuple of vectors of , called vector tuple, followed by a possibly empty tuple of possibly equal indices of , called index tuple. Each coordinate of the vector tuple is called a vector field, while each coordinate of the index tuple is called an index field. The set is partitioned into four sets: (for triples), (for couples), (for pairs), and (for indices). The names behind reflect the number and the nature (ordered or unordered) of the vector fields. Each of these sets comprise vertices with up to three vector fields and five index fields. They are defined in the following way.
-
•
: for every , we add vertex to . Thus vertices of have three vector fields and no index field.
-
•
: for every and such that and , we add vertex to . Thus vertices of have two vector fields and three index fields.
-
•
: for every and such that and , we add vertex to . We will still see and as filling the two vector fields of the vertex, without a first vector field and a second vector field. Contrary to vertices of , and are two names for the same vertex (whereas and are two distinct vertices, whose existence implies slightly different properties). Thus vertices of also have two vector fields and three index fields. Note also that is a multiset, since may be equal to .
-
•
: for every , we add vertex to . The chosen labels for the five index fields anticipate that, to build the edge set, it is convenient to imagine a separation after the first two index fields of the tuple. The vertices of have no vector field and five index fields.
Edge set
We will put some edges between and , and , and , and and . In addition, we put index-switching edges within and within . An index-switching edge is between two vertices of the same set ( or ) with the same vector tuple (which is always the case in ) and distinct index tuples. The only edges with a weight different than 1 are the edges between and , which all have weight 3. Thus, unless specified otherwise, an edge has weight 1.
The total list of edges is as follows.
-
•
We add all the index-switching edges within and . Thus is a clique and is a disjoint union of at most cliques (while and remain independent sets). More explicitly, we have an edge between every pair of distinct vertices and , and for every between every pair of distinct vertices and .
-
•
: We add an edge between every and provided that there is an such that .
-
•
: We add an edge between every and whenever .
-
•
: We add an edge of weight 3 between every and whenever .
-
•
: We add an edge between every and whenever or .
This ends the construction. See Figure 2 for an illustration.
Vertex and edge count
There are vertices in , , in , and , in , hence in total. There are edges in , , in , , in , , in , and in , hence edges in total. Furthermore can be built in time .
3.2 The absence of orthogonal quadruple implies diameter at most 4
Assuming that there is no orthogonal quadruple, we show that every pair of vertices of is at distance at most 4. For that we repeatedly use that, for every , is a well-defined index in . We only take the minimum index to have a deterministic notation, but there is nothing particular with it, and any index of the non-empty would work all the same.
We first observe that every vertex is at distance at most 3 from .
Lemma 3.1.
, , and .
Proof 3.2.
The first and second inclusions are actually equalities but we will not need those facts. since every is adjacent (with an edge of weight 1) to . Then, since every is adjacent to . Finally, since every is adjacent to for some , for otherwise is an orthogonal triple.
We now exhibit paths of length at most 4 between every pair of vertices of . For the case disjunction, initially imagine the with loops on vertices , where edges correspond to kinds of pairs that are left to check. The following paragraphs remove all its edges in the order: all edges incident to , all remaining edges incident to but , all remaining edges incident to , the loop on , and finally the edge .
Between and
As is a clique and, by Lemma 3.1, , every vertex is at distance at most 4 from every vertex .
Between and
For every , and so , by Lemma 3.1. In particular there is a path of length at most 4 between and any vertex .
Between and
Let be the two vector fields of , be the first two vector fields of , and be the third vector field of if . Let , if , and if . We observe that are (existing) vertices of , , and , respectively, and that is a path of length 3 in . The existence of these vertices is implied by , . The first edge of the path is an index-switching edge within . The existence of the other edges is implied by , , and the fact that the index tuple does not change.
Finally if , then the index-switching edge completes the -path of length 4. If instead , then the edge completes the -path of length 4. This edge exists since .
Between and
Let , , and . Then is a path of length 4 in . These vertices exist since and have value 1 on indices , , on indices , and , on indices . The first edge exists since , the next two edges exist for similar reasons as invoked in the previous paragraph, and the fourth edge exists since .
Between and
Let and . We set , , and exhibit a -path of length 4 via . Indeed is a path of length 4 in (recall that the first edge of the path has weight 3). Edge exists since . Edge exists since and the three last indices remain unchanged.
3.3 The presence of orthogonal quadruple implies diameter at least 7
Let be an orthogonal quadruple, that is, such that there is no index satisfying . We may further assume that are all distinct since checking for an orthogonal triple can be done in time . We will now show that there is no path of length at most 6 between and .
Since the distance between every pair of vertices in is at least 3, a -path of length at most 6 cannot contain an edge of the clique , nor more generally intersects at least twice. We thus distinguish two cases: (case A) visits exactly once, and (case B) remains within . Before proving that no -path of length at most 6 visits , thereby ruling out case A, we state a couple of useful observations.
Observation 3.3.
There is at most one path of length 2 between and , namely , which in particular implies that .
More basically, the only neighbors of (at distance 1, so not in ) are of the form . We can generalize this observation to paths contained in .
Observation 3.4.
For every path within , all the vertices of the path have the same first two vector fields.
Case A: visiting
As cannot visit twice, if it visits then it has length exactly 6 and is one of the following kinds: (case 1) , (case 2) , or (case 3) (recall that the edges in have weight 3). An important feature of such paths is that no index-switching edge can be used, thus the three last index fields (when they exist) have to remain the same.
Case 1. A path would in particular imply that , contradicting the orthogonality of .
Case 2. By 3.3 applied to both ends of the path, is of the form with some . The existence of the vertices implies that , and that and have value 1 on at least two indices (with multiplicity) of multiset . In particular, there is an such that , a contradiction to being orthogonal.
Case 3. By 3.3 applied to the second half of the path, has then the form . The first three vertices yield a contradiction. Indeed, the existence of edge implies that for some , while the existence of implies that .
Case B: paths within
We now consider paths in . Since , has to visit , since otherwise the first vector field cannot change, by 3.4. We then observe that no shortest -path visits a third time (one more time than the two endpoints and ). A -path visiting a third time would contain a segment that can be shortcut into . Indeed, has a chord which is an index-switching edge of .
We further distinguish two cases: (case 1) does not contain any index-switching edge, or (case 2) contains at least one index-switching edge.
Case 1. In that case, is of the form or . Either way, we consider the unique neighbors of and in . These neighbors have to be and for some . Indeed no index-switching edge nor return to is allowed here. Thus we conclude as in case A.2.
Case 2. We now assume that contains at least one index-switching edge (of ). In that case, as has length at most 6, it can visit only once. Hence is of the kind , where one of the two edges is optional. We consider the last vertex before visiting , and the first vertex after visiting . There is, by design, no index-switching edge between and on path . Thus by 3.4, there are such that and . We then conclude as in case A.2.
4 Removing the weights
So far we showed the announced lower bound for sparse undirected weighted Diameter. We show how to tune the previous construction to get the same lower bound for sparse undirected unweighted Diameter. The weighted graph had only non-trivial edge weights in . We now describe how to replace these weighted edges, to get an unweighted graph .
4.1 Unweighted construction
We start with a short summary of the changes. We will replace by three copies with an induced perfect matching between and , and between and . We link to as we linked to , and and , and and , as we linked and . We finally add a set of vertices with empty vector tuple (like ) that we link to and only.
Addition to the vertex set
We add three sets to to get : two identical copies of , denoted by and , and a set isomorphic to . More precisely, for every , we add vertex to . Thus has no vector field and a unique index field. We use a subscript to distinguish the homologous vertices in . Vertices are the three vertices of corresponding to the same vertex of .
Edition of the edge set
We first remove the edges of with weight 3 (between and ). For every , we add the edges and . We also add edges between and , the same way we have defined edges between and . That is, is an edge if and only if is an edge. Let us recall that the existence of this edge (and of its endpoint in ) implies that have value 1 on indices , three times, at least twice, and at least once, respectively, and that there is an such that .
We add edges (of weight 1) between and , the same way we defined the weight-3 edges of between and . Thus there is an edge whenever . We further add an edge between and whenever . Finally we add all the index-switching edges in , and we make and fully adjacent, that is, we turn into a clique.
This finishes the edition to the unweighted construction. See Figure 3 for an illustration.
New vertex and edge count
We added to vertices in , and , in . Thus has also vertices. We added to edges incident to , and edges incident to . (The edges between and were already counted in between and .) Thus has edges. Again can be computed in time .
4.2 The absence of orthogonal quadruple implies diameter at most 4
In case has no orthogonal quadruple, we use similar arguments as in , to find paths of length at most 4 between every pair of vertices in . We first show that is at distance at most 3 of every vertex of .
Lemma 4.1.
, , and .
Proof 4.2.
The inclusions are actually equalities. since is fully adjacent to and every is adjacent to some , for otherwise is an orthogonal pair. since every is adjacent to and every is adjacent to . Finally, since every is adjacent to and every is adjacent to .
We also show the following inclusions.
Lemma 4.3.
, and .
Proof 4.4.
, , and have all been shown in Lemma 4.1. Therefore we shall just prove that . Indeed every vertex is adjacent to some , since otherwise is an orthogonal triple.
For the case disjunction, initially imagine the with loops on vertices , where edges correspond to the kinds of pairs that are left to check. The following paragraphs remove all its edges in the order: all edges incident to and to , all remaining edges incident to and to but and , all remaining edges incident to , all remaining edges incident to as well the loop on , the edge , and finally the edge .
Between and
As is a clique and, by Lemma 4.1, , then holds for every vertex .
Between and
For every , by Lemma 4.3 and the fact that is a clique, and, again by Lemma 4.3, . In particular there is a path of length at most 4 from to any vertex .
The following two cases work as in , since and are twins in .
Between and
This holds by replacing the occurrence of by or , and every occurrence of by , in the paragraph Between and of the weighted construction.
Between and
Again this holds by replacing occurrences of (resp. ) by or (resp. or ).
Between and
This works as in by following three edges of weight 1 from to , instead of a single edge of weight 3. For every and , there is a path in , with and .
Between and
This case is the real novelty compared to , and the reason for introducing . For every and , there is a path in , with . The last two edges exist since .
4.3 The presence of orthogonal quadruple implies diameter at least 7
Again we assume that there is an orthogonal quadruple such that are pairwise distinct. We claim that there is no path of length at most 6 in between and . Since the distance between and is at least 3, any -path of length at most 6 visits at most once. For the sake of contradiction, let be such a path that we further assume shortest (hence in particular chordless) and, among shortest -paths, having the fewest edges in . We will show that cannot visit , nor use any edge of . Finally we observe that -paths of length at most 6 in respecting these two interdictions are in length-preserving one-to-one correspondence with -paths in .
cannot visit
The only possible kind of a -path of length at most 6 visiting is . This forces to be of the form for some . However the third and fourth edges imply that there is an such that , a contradiction to the orthogonality of .
cannot use any edge of
Assuming that contains at least one edge in , we first show that it has to contain a subpath or . Let be a vertex of with one neighbor on . The other neighbor of on is necessarily in . Indeed if , then , and is a chord. If instead , then one can replace the subpath by , contradicting the minimality of the number of used edges in (since this number decreases by 2).
Thus the only possibility is that . Then the other neighbor of on (other than ) has to be in , since otherwise is not a simple path. Hence contains a subpath of the kind (or the reverse, ). Now we observe that is at distance at least 1 from , while is at distance at least 3 from . Therefore such a path would have length at least 7.
Such a path would also exist in
We can now assume that does not use any vertex of nor any edge of . Every such simple -path (visiting at most once) also exists in the weighted graph , with the same length. To see it, we notice that if contains an edge , then it has to contain a subpath of the form , and is emulated in by taking the weight-3 edge . However we showed in the previous section that no -path of length at most 6 exists in .
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