Abstract 1 Introduction 2 Preliminaries 3 Low-treewidth shortcuttings of trees: lower bound 4 Low-arboricity shortcuttings 5 Routing schemes References

Tree-Like Shortcuttings of Trees

Hung Le ORCID University of Massachusetts Amherst, MA, USA    Lazar Milenković ORCID Tel Aviv University, Israel
INSAIT, Sofia University “St. Kliment Ohridski”, Bulgaria
   Shay Solomon ORCID Tel Aviv University, Israel    Cuong Than ORCID University of Massachusetts Amherst, MA, USA
Abstract

Sparse shortcuttings of trees – equivalently, sparse 1-spanners for tree metrics with bounded hop-diameter – have been studied extensively (under different names and settings), since the pioneering works of [24, 14, 2, 7], initially motivated by applications to range queries, online tree product, and MST verification, to name a few. These constructions were also lifted from trees to other graph families using known low-distortion embedding results. The works of [24, 14, 2, 7] establish a tight tradeoff between hop-diameter and sparsity (or average degree) for tree shortcuttings and imply constant-hop shortcuttings for n-node trees with sparsity O(logn). Despite their small sparsity, all known constant-hop shortcuttings contain dense subgraphs (of sparsity Ω(logn)), which is a significant drawback for many applications.

We initiate a systematic study of constant-hop tree shortcuttings that are “tree-like”. We focus on two well-studied graph parameters that measure how far a graph is from a tree: arboricity and treewidth. Our contribution is twofold.

  • New upper and lower bounds for tree-like shortcuttings of trees, including an optimal tradeoff between hop-diameter and treewidth for all hop-diameter up to O(loglogn). We also provide a lower bound for larger values of k, which together yield hop-diameter×treewidth=Ω((loglogn)2) for all values of hop-diameter, resolving an open question of [16, 19].

  • Applications of these bounds, focusing on low-dimensional Euclidean and doubling metrics. A seminal work of Arya et al. [4] presented a (1+ϵ)-spanner with constant hop-diameter and sparsity O(logn), but with large arboricity. We show that constant hop-diameter is sufficient to achieve arboricity O(logn). Furthermore, we present a (1+ϵ)-stretch routing scheme in the fixed-port model with 3 hops and a local memory of O(log2n/loglogn) bits, resolving an open question of [18].

Keywords and phrases:
spanner, tree shortcutting, arboricity, treewidth
Funding:
Hung Le: Supported by NSF grant CCF-2517033 and NSF CAREER Award CCF-2237288.
Lazar Milenković: Funded by a grant from the United States-Israel Binational Science Foundation (BSF), Jerusalem, Israel, and the United States National Science Foundation (NSF). This research was partially funded by the Ministry of Education and Science of Bulgaria (support for INSAIT, part of the Bulgarian National Roadmap for Research Infrastructure).
Shay Solomon: Funded by the European Union (ERC, DynOpt, 101043159). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Also funded by a grant from the United States-Israel Binational Science Foundation (BSF), Jerusalem, Israel, and the United States National Science Foundation (NSF).
Cuong Than: Supported by NSF grant CCF-2517033 and NSF CAREER Award CCF-2237288. Also supported by a Google Ph.D. Fellowship.
Copyright and License:
[Uncaptioned image] © Hung Le, Lazar Milenković, Shay Solomon, and Cuong Than; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Theory of computation Sparsification and spanners
Related Version:
Full Version: https://arxiv.org/abs/2510.14918
Editors:
Hee-Kap Ahn, Michael Hoffmann, and Amir Nayyeri

1 Introduction

Given a tree T=(V,E) and an integer k1, a tree shortcutting of T with hop-diameter k is a graph G=(V,E) such that for every two vertices u,vV, there is a path in G consisting of at most k edges such that δG(u,v)=δT(u,v), where δG(u,v) represents the distance between u and v in G. The problem of constructing sparse tree shortcuttings with small hop-diameter has been studied extensively (under different names and settings) since the pioneering works of [24, 14, 2, 7]. The optimal tradeoff is hop-diameter k with Θ(nαk(n)) edges, where αk(n) is a very slowly-growing inverse Ackermann function, α2(n)=logn, α3(n)=loglogn, α4(n)=logn, etc.111Throughout the paper, we use log(x)log2(x). (See the full version of the paper for a formal definition.)

Tree shortcutting has many applications, for example, finding max-flow [23], MST verification [23], maintaining MST under edge weight increases [23], computing semigroup product along paths and trees [24, 2], to name a few. In these applications, the shortcut graph serves as a compact data structure where k determines the query time, and the number of edges is the space. Tree shortcutting is a key subroutine in constructing low-hop (1+ϵ)-spanners for Euclidean [4, 22, 21] and doubling metrics [9, 18]. (See Section 2 for a formal definition.)

The fundamental drawback of sparse tree shortcuttings is that they are not uniformly-sparse – all of the aforementioned tree shortcutting constructions contain subgraphs with average degree of Ω(logn) for k=2 and Ω(n) for k=3. As k increases, the (global) sparsity of the shortcutting reduces substantially, but the uniform sparsity remains Ω(logn). Motivated by this, we initiate a systematic study of low-hop tree shortcuttings that are “tree-like”. In particular, we are interested in two notions that capture uniform sparsity: treewidth and arboricity. (See Section 2 for formal definitions.)

Low-treewidth shortcutting.

Low-treewidth shortcutting was introduced in the context of low-treewidth embedding of planar metrics. Specifically, Filtser and Le [16] showed that if one can construct a tree shortcutting with hop-diameter k and treewidth t, then one obtains an embedding of planar metrics into graphs of treewidth O(kt/ϵ) and additive distortion +ϵD, where D is the diameter of the input metric. They constructed a tree shortcutting with hop-diameter k=O(loglogn) and treewidth t=O(loglogn), and hence obtained an embedding with treewidth O((loglogn)2/ϵ). This bound does not seem to be optimal – other works [17, 11] used different embedding techniques to remove the dependency on n but at the cost of a (much) higher dependency on 1/ϵ. If one can construct a shortcutting with hop-diameter k=O(1) and treewidth t=O(αk(n)) (which matches the sparsity bound), then one gets an embedding of planar metrics with treewidth O(αk(n)/ϵ)=O(log(n)/ϵ), which is almost as good as O(1/ϵ). The linear dependency on 1/ϵ is optimal [16], whereas the other embedding techniques [17, 11] have an inherent Ω(1/ϵ2) barrier. Motivated by this observation, Filtser and Le [16] asked:

Question 1 ([19, 16]).

Is there a tree shortcutting with treewidth t and hop-diameter k such that kt=o((loglogn)2)? Furthermore, is it possible for kt to approach O(1)?

In this work, we seek to answer a broader question: What is the exact tradeoff between the hop-diameter k and the treewidth t of tree shortcuttings? Understanding the full tradeoff would not only answer Question 1 but could also be useful for other applications that require a different hop-diameter. Specifically, in this paper, we use our construction for k=3 to resolve an open problem posed by Kahalon et al. [18] regarding compact routing schemes.

Low-arboricity shortcutting.

Given a set of points P in an Euclidean (or doubling) space of dimension d, one can construct a sparse (1+ϵ)-spanner with a small hop-diameter. This is achieved via tree covers. (See Section 2 for a definition.) Known tree cover constructions for Euclidean and doubling metrics achieve stretch 1+ϵ using ϵO(d) trees [4, 5, 12]. By constructing a k-hop shortcutting GT for each tree T in the cover 𝒯, the union T𝒯GT is a k-hop (1+ϵ)-spanner. For a constant ϵ and d, the sparsity is only O(1) times worse than the sparsity of the shortcuttings, which is O(αk(n)). However, if GT has low treewidth for every tree T, the union T𝒯GT might have a very large treewidth. A recent work on low-treewidth Euclidean spanners by Buchin, Rehs, and Scheele [8] showed that treewidth-t spanners must have stretch Ω(n/td/(d1)). That is, if the stretch is O(1), the treewidth has to be very large: Ω(n11/d).

While the treewidth grows substantially under taking union, the arboricity does not: if GT has arboricity at most β for every T𝒯, then T𝒯GT has arboricity |𝒯|β, which is O(β) for constant ϵ and d. This makes arboricity an appealing tree-like measure. Specifically, one could hope to construct a low-arboricity Euclidean (or doubling) (1+ϵ)-spanner with small hop-diameter using low-arboricity tree shortcuttings.

1.1 Our contribution

Our key conceptual contribution is in identifying the power of tree-like shortcuttings of trees, in particular in the regime of very small hop-diameter. We employ the low-treewidth construction with hop-diameter 3 to improve the state-of-the-art on low-hop compact routing schemes in tree and doubling metrics. Using the low-arboricity shortcuttings, we devise a (1+ϵ)-spanner with arboricity O(logn) and hop-diameter 6 for doubling metrics. We also fully resolve Question 1 in the negative, which is our main technical contribution.

Treewidth.

Our main technical contribution is a lower bound on the treewidth t of tree shortcuttings for any hop-diameter k, which holds even when the underlying graph is a path. This result fully resolves Question 1 in the negative.

Theorem 2.

For every n1, every shortcutting with hop-diameter k for an n-point path must have treewidth:

  • t=Ω(klog2/kn) for even k and t=Ω(k(lognloglogn)2/(k1)) for odd k3, whenever k2ln(2e)lnlogn;

  • t=Ω((loglogn)2/k) whenever k>2ln(2e)lnlogn.

Specifically, Theorem 2 implies that tk=Ω((loglogn)2), and therefore, the artificially looking upper bound by Filtser and Le [16] is indeed optimal.

We prove Theorem 2 by constructing a large clique minor Kt in any given shortcutting H with hop-diameter k. Such a clique minor is a certificate that the treewidth of G is at least t. Our lower bound instance is an n-vertex path, and the proof is by induction on k. We observe that when k=2, shortcutting H contains the clique Klogn+1 as a minor. For a higher even value of k4, we use the induction hypothesis for k2. We divide the path into equally sized subpaths and consider two cases. If there is a subpath where every vertex has an edge going out of it, we can readily construct a large minor using this subpath. Otherwise, each subpath P has at least one vertex without an edge going out of P. This allows us to use the induction hypothesis for k2 on a graph obtained by contracting each subpath into a single vertex. The argument for odd values of k is analogous. Although the method of constructing the minor is simple to describe, the analysis is much more intricate, specifically handling the dependency between k and n in the recurrences. See Section 3 for details.

We also give an upper bound construction that matches the lower bound when k=O(loglogn), which arguably is the most interesting regime. Our construction is a rather natural adaptation of the known result by Filtser and Le [16]. The difference is that when we shortcut a set of “cut vertices” recursively, we set the hop bound to be k2. This requires some technical work; the proofs are deferred to the full version. The following theorem summarizes the construction.

Theorem 3.

For every n1 and every k=O(loglogn), every n-vertex tree admits a shortcutting with hop-diameter k and treewidth O(klog2/kn) for even k and O(k(lognloglogn)2/(k1)) for odd k3.

In Section 5, we use this construction to devise a 3-hop routing scheme for tree metrics with stretch 1 and O(log2n/loglogn) bits per vertex. This answers the open question from [18]: “Whether or not one can use a spanner of larger (sublogarithmic and preferably constant) hop-diameter for designing compact routing schemes with o(log2n) bits is left here as an intriguing open question.” In the full version of the paper, we prove that the bound is tight. Using tree covers, we obtain a 3-hop routing scheme with stretch (1+ϵ) that uses ϵO~(d)log2n/loglogn bits per vertex for every n-point metric with doubling dimension d. (See Section 5.)

Theorem 4.

For every n and every n-point metric with doubling dimension d, there is a 3-hop routing scheme with stretch (1+ϵ) that uses ϵO~(d)log2n/loglogn bits per vertex.

This provides the first routing scheme in Euclidean and doubling metrics, where the number of hops is o(logn), and the labels consist of o(log2n) bits.

Arboricity.

In Section 4 we focus on low-arboricity shortcuttings. We show how to construct shortcuttings of hierarchically separated trees (HSTs) where the arboricity grows proportionally to an inverse Ackermann function.

Our starting point is a sparse shortcutting of an n-vertex path. We use the well-known fact about arboricity – if the edges of a given graph can be oriented so that the in-degree is bounded by β, then the arboricity is at most β+1. Our key observation is that one can split every edge (u,v) of a sparse shortcutting into two edges (u,w) and (w,v) in order to reduce the in-degrees of u and v. By carefully choosing vertex w for every edge (u,v), we show that the arboricity can be bounded by αk/2+1(n) when the hop-diameter is k. Roughly speaking, we achieve the same sparsity bound with a twice as large hop-diameter, which is the consequence of splitting each edge into two. Our construction for paths is then readily applicable to HSTs. Using the known HST cover [15], we obtain the following theorem. See Section 4 for details.

Theorem 5.

Let k be an even integer and let ϵ(0,1/6) be an arbitrary parameter. Then, for every positive integer n, every n-point metric with doubling dimension d admits a (1+ϵ)-spanner with hop-diameter k and arboricity ϵO(d)αk/2+1(n).

This significantly strengthens the construction of Arya et al. [4], by providing a uniformly sparse (rather than just sparse) construction with constant hop-diameter: For hop-diameter k, we transition from sparsity ϵO(d)αk(n) to arboricity ϵO(d)αk/2+1(n). In particular, we get arboricity O(logn) with a hop-diameter of 6. Recall that, in contrast to arboricity, one cannot achieve similar dependencies on the treewidth.

For general trees, we obtain a k-hop shortcutting with arboricity O(log12/(k+4)n). While the arboricity is larger than that of ultrametrics (cf. Lemma 16), it is smaller than the treewidth in Theorem 3 by about a factor of k (at a cost of some slack to the exponent of logn). Specifically, when k=Θ(loglogn), the arboricity is O(1), while the treewidth is Ω(loglogn). Using this result and the known tree cover constructions [10, 11, 5, 20], we obtain low-hop spanners for planar, minor-free, and general metrics with small arboricity. See the full version for details.

2 Preliminaries

Treewidth.

A tree decomposition of G=(V,E) is a tree T where each node is associated with a subset of V called a bag. The bags X1,,X satisfy: (i) i=1Xi=V, (ii) for every vertex vV, the collection of bags containing it forms a connected subtree of T, and (iii) for every edge (u,v)E, there is a bag containing u and v. The width of T is maxi=1|Xi|1; the treewidth of G is the minimum width among all possible tree decompositions of G. If a graph G contains Kh as a minor, then the treewidth of G is at least h1.

Arboricity.

The arboricity of G=(V,E) is defined as max|E(H)||V(H)|1, where the maximum is taken over all subgraphs H of G with at least two vertices.

Spanners.

Given a metric space MX=(X,δ), which we view as a complete graph (X,(X2)), a t-spanner of MX is a subgraph H=(X,E) with E(X2), such that for every two vertices x,yX, their distance in H is at most tδ(x,y). The path Px,y realizing this distance is called a t-spanner path. The hop-diameter of a spanner is the minimum k such that between every two vertices x,yX there is a t-spanner path with at most k edges.

Tree covers.

Given a metric space MX=(X,δ), a tree cover of MX with stretch t is a collection of trees such that for every two vertices x,yX we have δ(x,y)minT𝒯δT(x,y)tδ(x,y), where δT(x,y) is the distance between x and y in T.

3 Low-treewidth shortcuttings of trees: lower bound

In this section, we prove Theorem 2, restated below for the reader’s convenience.

Theorem 2. [Restated, see original statement.]

For every n1, every shortcutting with hop-diameter k for an n-point path must have treewidth:

  • t=Ω(klog2/kn) for even k and t=Ω(k(lognloglogn)2/(k1)) for odd k3, whenever k2ln(2e)lnlogn;

  • t=Ω((loglogn)2/k) whenever k>2ln(2e)lnlogn.

Given an arbitrary integer n, our instance is the n-point path. We argue that any shortcutting of the path has a large minor. Due to the inductive nature of our argument, we prove a stronger version of the statement, which considers shortcuttings that could potentially use points outside of the given path. We start with the proof for hop-diameter k=2.

Lemma 6.

For every n1 and every n-vertex path L, every shortcutting with hop-diameter 2 has Klogn+1 as a minor.

Proof.

We prove the claim by complete induction over n. For the base case, we use n=1, where the claim holds vacuously. Let H be a shortcutting for L with hop-diameter 2. Split L into two consecutive parts, L1 and L2, of sizes n/2 and n/2, respectively. Let H1 and H2 be subgraphs of H induced on L1 and L2, respectively. From the induction hypothesis, H1 and H2 have Klogn/2+1 and Klogn/2+1 as minors, respectively. Consider the case where every point of L1 has an edge in H to some point in L2. Then Klogn/2{H2} induces a clique minor of size logn/2+1. Consider the complementary case where L1 has a point p that does not have an edge in H to any point in L2. Then, every point in L2 has a neighbor in L1 because H has hop-diameter 2. Thus, Klogn/2{H1} induces a clique minor of size logn/2+1. Hence, the minor size satisfies recurrence W2(n)W2(n/2)+1, with a base case W2(1)=1. The solution is given by W2(n)=logn+1.

We next give a proof for even values of k such that 4k2ln(2e)lnlogn in Lemma 7. The proof for odd values is analogous. The proof for k>2ln(2e)lnlogn is given in Lemma 8. We will need some auxiliary claims (Claims 9, 11, and 10) in the proofs of Lemmas 7 and 8, as well as in the upper bound proof of Theorem 3. These claims are deferred to the end of this section.

Lemma 7.

For every n1, every even k such that 4k2ln(2e)lnlogn, and every n-vertex path L, every shortcutting with hop-diameter k has treewidth at least c1klog2/kn1, where c1 is an absolute constant.

Proof.

We prove the statement by complete induction over n and k. For the base case, we take n=1, and Wk(1)=1>c1klog2/kn1. Otherwise, let k be such that logk=(kk2)(k2)/2(logn)(k2)/k for even values of k. (The proof for odd values is similar. There, we choose k so that logk=(kk2)(k2)/2(logn/loglogn)(k2)/k.) By Claim 10, we have that kn, whenever k2ln(2e)lnlogn.

Split L into consecutive sets of points L1,L2,,Lk of size n/k each and ignore the remaining points. Let H be a shortcutting with hop-diameter k for L. Our goal is to show that the size of a clique minor of H can be lower bounded by the following recurrence.

Wk(n)min(Wk2(k),Wk(n/(2k))+1) and Wk(1)=1 (1)

We say that a point in Li is global if it has an edge to a point outside Li and non-global otherwise. We say that Li is global if all of its points are global and non-global otherwise.

Figure 1: An illustration of Case 1. Every set Li contains a non-global point. Any k-hop path between two non-global points must have the first and the last edge inside the corresponding sets. By contracting every Li into a single point, we obtain a new path L. Any shortcutting with hop-diameter k for L must have hop-diameter k2 for L. This allows us to use the induction hypothesis for k2. In the illustration above the blue path consisting of 4 hops in L becomes a 2-hop path in L.

Case 1: Every 𝑳𝒊 is non-global.

See Figure 1 for an illustration. For every Li and Lj the path between a non-global point in Li and a non-global point in Lj must have the first (resp., last) edge inside Li (resp., Lj). Let H be obtained from H by contracting each Li into a single vertex. (Clearly, H[Li] is connected.) Let L be the path obtained from L by contracting every Li into a single point. Then H is a (k2)-hop spanner of L with stretch 1. Thus, H has a minor of size Wk2(k)c1(k2)log2/(k2)k1=c1klog2/kn1.

Case 2: Some 𝑳𝒊 is global.

See Figure 2 for an illustration. Let {Ll,Lr}=LLi, so that Ll (resp., Lr) is on the left (resp., right) of Li. (Possibly Ll= or Lr=.) At least |Li|/2 points in Li have edges to either Ll or Lr. Without loss of generality, we assume the latter. Let LiLi be the subset of points that have edges to Lr and let Hi be a subgraph of H which is a shortcutting of Li with hop-diameter k. Inductively, Hi has a clique minor of size at least Wk(n/(2k)). (Since Hi is a 1-spanner, it does not include any point outside of Li.) Then Hi and Lr are vertex-disjoint (because we are considering 1-spanners) and hence their union has a clique minor of size Wk(n/(2k))+1. Thus, Wk(n) satisfies Equation 1, which we lower bound next.

Figure 2: An illustration of Case 2. L2 is global, meaning that every vertex is adjacent to an edge going outside of L2. At least half of these edges are adjacent on the vertices in Lr. By contracting Lr and using the clique minor of L2, we obtain the desired clique minor.
Wk(n) min(c1k(logn)2/k1,Wk(n/(2k))+1)
min(c1k(logn)2/k1,c1k(lognlogk1)2/k)
min(c1k(logn)2/k1,c1k(logn(kk2)k22(logn)(k2)/k1)2/k)
min(c1k(logn)2/k1,c1k(logn)2/k1)

The last inequality follows from Claim 9 by replacing c1=1/γ, x=logn and α=1.

Lemma 8.

For every n1, every k>2ln(2e)lnlogn, every shortcutting with hop-diameter k for an n-vertex path has treewidth at least c2(loglogn)2/k, for an absolute constant c2.

Proof.

We set k so that loglogk=k2kloglogn. (We use log()log2().) For the clarity of exposition, we ignore the rounding issues. We note that 1kn. Using the same argument as in Lemma 7, we have Wk(n)min(Wk2(k),Wk(n/(2k))+1). We prove the lemma by induction, where the base case is Lemma 7 whenever k<2ln(2e)lnlogn. Our goal is to prove that Wk2(k)c2(loglogn)2/k and Wk(n/(2k))+1c2(loglogn)2/k. For the first inequality we distinguish two cases. If k22ln(2e)lnlog(k), then by Lemma 7 we have

Wk2(k) c1(k2)log2k2k1c1(k2)logln(2e)lnlogkk1=2ec1(k2)1
ec1k1ec1(2ln(2e))2(lnlogk)2k1c2(loglogk)2k2

The penultimate inequality holds because k2ln(2e)lnlog(n)2ln(2e)lnlog(k). The last inequality holds for a proper choice of constant c2. If k2>2ln(2e)lnlog(k), we have Wk2(k)c2(loglogk)2k2 by the induction hypothesis. Hence, in both cases, we have:

Wk2(k) c2(loglogk)2k2=c2(k2kloglogn)2k2=c2(loglogn)2k

For the second inequality, let x=logn. We have logk=x(k2)/k. Since n2k<n, we have that k>2ln(2e)lnlogn2k and the induction hypothesis gives the following.

Wk(n2k)+1c2(loglog(n2k))2k+1=c2log2(xx(k2)/k1)k+1

To show that the right-hand side is at least c2log2(x)/k, it suffices to show the following:

log2(xx(k2)/k1)+kc2log2x (2)

From Claim 11 we have x(k2)/kxxlnxk+xln2xk2.

xx(k2)/k1x(xxlnxk+xln2xk2)1=xlnxk(1lnxk)1>xlnx10k1

The last inequality holds because k>2ln(2e)ln(x). We next consider two cases. If xlnx10k<2, then kc2>xlnx20c2log2x and Equation 2 is proved. Otherwise, we proceed as follows.

log2(xlnx10k1)+kc2 log2(xlnx20k)+kc2=(logx+loglnx20k)2+kc2
=log2x+2(logx)loglnx20k+log2lnx20k+kc2log2x

The last inequality holds for any c21/10.

We conclude the section by proving the auxiliary claims used in the lemmas above.

Claim 9.

There is an absolute constant γ such that for α{0,1}, every integer k4 and every x>1 where the expression is defined, it holds

2k<x2/k(x(kk2)(k2)/2x(k2)/kα)2/k<γk

Proof.

We rewrite the expression as follows.

x2/k(1(1(kk2)(k2)/2x2/kαx1)2/k) (3)

Using Maclaurin expansion, we have that (1+y)2/k=1+2ky+2kk2(1+ζ)2k2y2, where ζ is a number between 0 and y. We set y=(kk2)(k2)/2x2/kαx1.

(1(kk2)(k2)/2x2/kαx1)2/k= (4)
12k((kk2)k22x2k+αx1)k2k2(1+ζ)2k2((kk2)k22x2k+αx1)2 (5)

Plugging Equation 4 into Equation 3, we obtain the following.

x2/k(1(1(kk2)k22x2/kαx1)2/k)=
2k((kk2)k22+αxk2k)+k2k2(1+ζ)2k2((kk2)k22x1/k+αxk1k)2

The lower bound in Claim 9 holds because 1<y<ζ<0 and x>1. Next we prove the upper bound.

2k((kk2)k22+αxk2k)+k2k2(1+ζ)2k2((kk2)k22x1/k+αxk1k)2<
2(e+x(k2)/k)+(ex1/k+x(k1)/k)2k

The right-hand side is decreasing with x in the whole domain and we can upper bound it by taking x=1.

2(e+x(k2)/k)+(ex1/k+x(k1)/k)2k<(e+1)(e+3)k

Letting γ=(e+1)(e+3), the upper bound from Claim 9 follows.

Claim 10.

For every 3k2ln(2e)lnlogn, it holds that (kk2)(k2)/2(logn)(k2)/k(logn)/2.

Proof.

We have that k2ln(2e)lnlogn. Rearranging the last inequality, we have that e(logn)(k2)/k(logn)/2. The proof is completed by observing that (kk2)(k2)/2 is monotonically increasing for k3 and limk(kk2)(k2)/2=e.

Claim 11.

For every x1 and k4, x(k2)/kxxlnxk+xln2xk2

Proof.

Let p1/k. Then, x(k2)/k=x12p. Using Taylor series around p=0, we have that x12p=xpxlnx+R(p). The term R(p) has the following form for 0<ξ<p.

R(p)=p22x12ξlnx(lnx12ξ(12ξ)3/2)p22x12ξlnxlnx12ξp2xln2x

To complete the proof, we replace p by 1/k.

4 Low-arboricity shortcuttings

In this section, we prove Theorem 5. See 5 We first describe the shortcutting on paths (Section 4.1) and then adapt it to HSTs via an analogous argument (Section 4.2). Instead of directly arguing about the arboricity, we show that the shortcutting can be oriented so that it has a bounded in-degree. The connection is provided by the following fact.

Fact 12.

If the edges of a graph G=(V,E) can be oriented such that the maximum in-degree of every vertex is at most d, then the arboricity of G is at most d+1.

4.1 Paths

Figure 3: An illustration of the shortcutting with hop-diameter 4 and arboricity O(loglogn). We start by a well-known 3-hop construction [2, 7]. Split the path into subpaths using n cut vertices (red) so that every subpath contains n vertices. The shortcutting is obtained by: (i) interconnecting the cut vertices by a clique, (ii) connecting every cut vertex to the points in its two adjacent subpaths (in the illustration, only two such edges, (u,u) and (v,v), are shown in green), and (iii) applying the recursive construction to each of the subpaths. This construction readily gives hop-diameter 3 (see, e.g., the path between u and v), but it suffers from having arboricity Ω(n) due to the clique on the cut vertices. To remedy this, we split every clique edge (u,v) into two edges, (u,w) and (w,v). This allows us to reduce the in-degree of every point to O(1) per recursion level, at a cost of increasing the hop-diameter to 4. Overall, the number of recursion levels is O(loglogn), yielding the desired bound on the arboricity.

The main intuition of this part of the proof is given in Figure 3. We shall use a modification of the following well-known result.

Theorem 13 (Cf. [3, 7, 22]).

For every n2 and k2, every n-point tree metric admits a shortcutting with hop-diameter k and O(nαk(n)) edges.

We next prove a slightly modified version of this theorem.

Lemma 14.

Let n1 and k1 be two arbitrary integers. Let L be a line metric induced by a set of n points on a line so that between every two points there are αk(n) Steiner points for k2 and n2 points for k=1. Then, L admits a Steiner 1-spanner with hop-diameter 2k such that the Steiner points belong to S and every vertex in LS has a constant in-degree.

Proof.

We prove the lemma by induction over k. For k=1, we interconnect the vertices in L by a clique. Consider an arbitrary clique edge (u,v) and split it into two using a Steiner point w. Orient the edges (u,w) and (w,v) into w. By using a fresh Steiner point for every clique edge, we obtain the guarantees from the statement. For k=2, we take a central vertex c in L and connect it to every other point in L; orient the edges outwards from c. Proceed recursively with the two halves. This way we obtain a 1-spanner for L with hop-diameter 2. Denote by E the edge set of this spanner. The depth of the recursion in the construction is O(logn), and the size of S is nα2(n)=nlogn. Every vertex in L has in-degree 1 per recursion level. We can split every such edge into two using a fresh Steiner point for each recursion level. This concludes the proof for k=2.

Consider now an arbitrary k. Divide L into intervals of size αk2(n) using n/αk2(n) cut vertices. Denote by C the set of cut vertices and invoke the induction hypothesis on C with parameter k2. Let E be the obtained set of edges. Let EC be obtained by connecting every cut vertex to every point in the two neighboring intervals; the edges are oriented out of EC. Proceed recursively with parameter k on each of the intervals.

To analyze the in-degree, we observe that the depth of the recursion with parameter k is O(αk(n)), which coincides with the number of Steiner vertices between every two consecutive points in L. One level of recursion contributes a constant in-degree to each vertex in the construction. This means that we can split all such vertices into two and use a fresh Steiner point at each recursion level. This concludes the proof.

Proposition 15.

For every n1 and every even k2, every n-point path admits a shortcutting with hop-diameter k and arboricity O(αk/2+1(n)).

Proof.

Let Ln be an arbitrary line metric. For an integer k2, we describe a construction of a shortcutting H for Ln with hop-diameter 4k2 and arboricity O(α2k(n)).

Consider a set of n=n/α2k2(n) equally-spaced cut vertices dividing Ln into subpaths consisting of α2k2(n) points. To construct the shortcutting H, we connect every cut vertex to all the vertices in its two neighboring intervals. Denote the corresponding edge by EC. Let C be the set of cut vertices. Let E be obtained by invoking Lemma 14 with parameter 2k2 on C using LnC as Steiner points. Proceed recursively with each of the intervals.

To analyze the arboricity, we will show that the edges in EC and E can be oriented so that the in-degree of every vertex is constant. Orient every edge in EC so that it goes out of the corresponding cut vertex. Since every interval is adjacent to at most two cut vertices, the in-degree of every point with respect to EC is at most 2. By Lemma 14, the edges in E have a constant in-degree on Ln. In conclusion, EC and E contribute O(1) to the in-degree of each vertex in Ln. The number of recursion levels (n) satisfies the recurrence (n)=(α2k2(n))+O(1), which has solution (n)=α2k(n). Every recursion level contributes O(1) to the in-degree of vertices, meaning that the overall in-degree in H is O(α2k(n)). The hop-diameter of H is 2+2(2k2)=4k2. This concludes the description of a shortcutting with hop-diameter 4k2 and arboricity O(α2k(n).

Note that we have only shown how to get a tradeoff of hop-diameter 4k2 and arboricity O(α2k(n)). We could similarly get a construction with hop-diameter 4k and arboricity O(α2k+1(n)) for a parameter k1. Specifically, we divide Ln into intervals of size α2k1(n). The cut vertices are interconnected using Lemma 14 with hop-diameter 2k1. The hop-diameter is 2+2(2k1)=4k and the arboricity is O(α2k+1(n)) using a similar argument.

4.2 Ultrametrics and doubling metrics

A metric (X,dX) is an ultrametric if it satisfies a strong form of the triangle inequality: for every x,y,z, dX(x,z)max{dX(x,y),dX(y,z)}. It is well known that an ultrametric can be represented as a hierarchical well-separated tree (HST). More precisely, an (s,Δ)-HST is a tree T where (i) each node v is associated with a label Γv such that ΓvsΓu whenever u is a child of v and (ii) each internal node v has at most Δ children. Parameter s is called the separation while Δ is called the degree of the HST. Let L be the set of leaves of T. The labels of internal nodes in T induce a metric (L,dL) on the leaves, called leaf-metric, where for every two leaves u,vL, dL(u,v)=Γlca(u,v) where lca(u,v) is the lowest common ancestor of u and v. It is well-known, e.g., [6], that (L,dL) is an ultrametric, and that any ultrametric is isomorphic to the leaf-metric of an HST.

Chan and Gupta [9] showed that any (1/ϵ,2)-HST can be embedded into the line metric with (worst-case) distortion 1+O(ϵ). Therefore, by applying Proposition 15, we obtain a (1+O(ϵ))-spanner with hop diameter k and arboricity O(αk/2+1(n)) for (1/ϵ,2)-HSTs. In our setting, we are interested in large-degree (s,Δ)-HSTs where s=1/ϵ and Δ=poly(1/ϵ); the embedding result by Chan and Gupta [9] no longer holds for these HSTs. Instead, we directly apply our technique for the line metric to get a (1+O(ϵ))-spanner with low-hop diameter. We describe the modifications of Proposition 15 required to prove Lemma 16.

Lemma 16.

Let ϵ(0,1), Δ>0 be parameters, and k be an even positive integer. Then, any (Θ(1/ϵ),Δ)-HST with n leaves admits a (1+ϵ)-spanner with hop-diameter k and arboricity O(αk/2+1(n)).

Proof.

Let T be the (1/ϵ,Δ)-HST and let MT be the metric induced by T. For an integer k2, we describe a construction of a (1+O(ϵ))-spanner for MT with hop-diameter 4k2 and arboricity O(α2k(n)). The construction is similar to that in Proposition 15.

Let C be the set of internal nodes of T, called cut vertices, such that the subtrees rooted at these nodes has size α2k2(n). The number of cut vertices is |C|n/α2k2(n). First, connect every cut vertex to all of its descendants in T and let the corresponding set of edges be EC. Next, let E be the set of edges interconnecting the cut vertices using Theorem 13 with hop-diameter 2k2 and O(nα2k2(n))=O(n) edges. We construct a set E by subdividing every edge (u,v)E into two edges using a vertex, say x, in the subtree rooted at u. The spanner H is obtained using the edges in EC and those in E. Finally, the recursive construction is applied to each subtree rooted at a vertex in C. This concludes the description of the recursive construction of H. The same argument in Proposition 15 implies that the arboricity is O(α2k(n)). The stretch is (1+O(ϵ)) since the path uxv between two cut vertices u and v has stretch (1+O(ϵ)).

To construct a low-hop spanner with small arboricity for doubling metrics (Theorem 5), we will rely on the ultrametric cover by Filtser and Le [15]. Following their notation, for a given metric space (X,dX), we say that a collection 𝒯 of at most τ different (s,Δ)-HSTs such that (i) for every HST T𝒯, points in X are leaves in T, and (ii) for every two points x,yX, dX(x,y)dT(x,y) for every T𝒯, and there exists a tree Txy𝒯 such that dX(x,y)ρdTxy(x,y).

Theorem 17 (Cf. Theorem 3.4 in [15]).

For every ϵ(0,1/6), every metric with doubling dimension d admits an (ϵO(d),1+O(ϵ),1/ϵ,ϵO(d))-ultrametric cover.

Let 𝒯 be the (ϵO(d),1+O(ϵ),1/ϵ,ϵO(d))-ultrametric cover in Theorem 17 for the input doubling metric. We construct the spanner from Lemma 16 on each of (1/ϵ,ϵO(d))-HST in 𝒯 and take the union of them. This concludes the proof of Theorem 5.

5 Routing schemes

A compact routing scheme is a distributed algorithm that uses local information (routing tables and unique labels) to deliver packets between any source and destination node in a network. In this section, we prove Theorem 4. See 4 Our starting point is the routing scheme for the trees.

Theorem 18.

For every n and every n-vertex tree T, there is a 3-hop routing scheme in the fixed-port model for the metric MT induced by T with stretch 1 that uses O(log2n/loglogn) bits per vertex.

Proof.

Given an n-vertex tree T, our routing scheme operates on top of a shortcutting with hop-diameter 3 and treewidth O(logn/loglogn). We start by sketching this construction. Note that the routing scheme does not rely on the treewidth bound itself, but on the fact that the edges of the shortcutting can be oriented so that every vertex has an in-degree bounded by O(logn/loglogn).

Let X be a set of vertices of T such that: (i) every component of TX is of size O(nloglogn/logn), (ii) the size of X is O(logn/loglogn), (iii) every component of TX has at most 2 edges adjacent to X, and (iv) if a component C has 2 edges adjacent to some x,yX, then x and y are in ancestor/descendant relation. Connect the vertices of X by a clique and do the following for every subtree T in TX. Let u and v be two vertices from X adjacent to T. Connect u and v to every vertex in T and proceed recursively with T. When n3, connect the vertices by a clique. This concludes the description of the shortcutting. The constructions of low-treewidth shortcuttings for general values of hop-diameter are more technical (and not needed for this section). See the full version for the details.

Our routing scheme is constructed on top of a 3-hop shortcutting H3 described above. For a vertex uT, let table(u) denote its routing table and label(u) its label. First, assign a unique identifier ID(u){1,,n} to every vertex u in T and add it to table(u) and label(u). Equip the routing table and a label of every vertex uT with an ancestor label anc(u) as in [1]. Using this ancestor labeling scheme, we can determine, given two vertices u and v, whether they are in ancestor-descendant relation, and if so, whether u is the ancestor of v or not. This adds O(logn) bits of memory per vertex.

Recall the recursive construction of the shortcutting. Assign to each recursive call a unique integer rT. For every vertex in uX, add to table(u) the information consisting of C(u)=rT,{ID(v),port(u,v),anc(v)vX{x}}. The memory occupied per every vertex in X is O(log2n/loglogn). (Note that the construction of H3 guarantees that every vertex belongs to such a clique exactly once across all the recursive calls, meaning that table(u) contains only one such C(u).) Let T be a subtree in TX. Let u and v be two vertices from X to which T has outgoing edges. For every vertex xT, add to table(x) the following: rT,ID(u),port(x,u) and rT,ID(v),port(x,v). Similarly, add to label(x) the following: rT,ID(u),port(u,x) and rT,ID(v),port(v,x). This information takes O(logn) bits per recursive call rT. The construction proceeds recursively with T; the number of recursive calls every vertex participates in is at most O(logn/loglogn).

Next we describe the routing algorithm. Let u be the source and v be the destination. First, check if C(u) contains routing information leading directly to v. In this case, the algorithm outputs port(u,v) and the routing is complete. (This case happens when u and v are in the same clique during the construction.) Otherwise, go over table(u) and label(v) and find the last recursive call rT which is common to both u and v. Next, consider label(v) and the two entries consisting rT,ID(v1),port(v1,v) and rT,ID(v2),port(v2,v), corresponding to rT. If v1 and v2 are in C(u), use anc(u), anc(v1), anc(v2), and anc(v) to decide whether to output port(u,v1) or port(u,v2). (This case happens when u, v1, and v2 are in X in the recursive call rT and v is not in it.) Finally, let rT,ID(u1),port(u,u1) and rT,ID(u2),port(u,u2) be the two entries corresponding to rT in table(u). Use anc(u) and anc(v) to decide whether to output port(u,u1) or port(u,u2). (This case happens when u is not in X.)

In what follows, we extend the routing result for tree metrics to the metrics with doubling dimension d. We first construct a tree cover using the tree cover theorem from [13].

Theorem 19 ([13]).

Given a point set P in a metric of constant doubling dimension d and any parameter ϵ(0,1), there exists a tree cover with stretch (1+ϵ) and ϵO~(d) trees. Furthermore, every tree in the tree cover has maximum degree bounded by ϵO(d).

We use this specific tree cover theorem, since the authors also provide an algorithm for determining the “distance-preserving tree” given the labels of any two metric points.

Lemma 20 ([13]).

Let ϵ(0,1). Let T={T1,,Tk} be the tree cover for P constructed by Theorem 19, where k=ϵO~(d). There is a way to assign ϵO~(d)logn-bit labels to each point in P so that, given the labels of two vertices x and y, we can identify an index i such that tree Ti is a “distance-approximating tree” for u and v; that is, δTi(x,y)(1+ϵ)δP(x,y). This decoding can be done in O(dlog(1/ϵ)) time.

We equip each tree in the cover with the stretch-1 routing scheme from Theorem 18. This consumes overall ϵO~(d)log2n/loglogn bits per point in P. In addition, we add ϵO~(d)logn-bit labels to each point in P as stated in Lemma 20. Given two points x,yP, we first employ the algorithm from Lemma 20 to find the tree in which the routing should proceed. Then, the routing proceeds on that specific tree using the routing algorithm from Theorem 18. This concludes the proof of Theorem 4.

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