1 Introduction
Let be a graph, possibly with edge lengths . A spanner of , for , is a subgraph that preserves all pairwise distances within factor , i.e.,
(1) 
for all (where denotes the shortestpath distance in a graph ). The distance preservation factor is called the stretch of the spanner. Less formally, graph spanners are a form of sparsifiers that approximately preserve distances (as opposed to other notions of graph sparsification which approximately preserve cuts [BK15], the spectrum [SS11, BSS14], or other graph properties). When considering spanners through the lens of sparsification, perhaps the most important goal in the study of graph spanners is understanding the tradeoff between the stretch and the sparsity. The main result in this area, which is tight assuming the “Erdős girth conjecture” [Erd64], was given by Althöfer et al.:
Theorem 1 ([Add93]).
For every positive integer , every weighted graph has a spanner with at most edges.
This notion of graph spanners was first introduced by Peleg and Schäffer [PS89] and Peleg and Ullman [PU89] in the context of distributed computing, and has been studied extensively for the last three decades in the distributed computing community as well as more broadly. Spanners are not only inherently interesting mathematical objects, but they also have an enormous number of applications. A small sampling includes uses in distance oracles [TZ05], property testing [BGJ09, BBG14], synchronizers [PU89], compact routing [TZ01], preprocessing for approximation algorithms [BKM09, DKN17]), and many others.
Many of these applications, particularly in distributed computing, arise from modeling computer networks or distributed systems as graphs. But one aspect of distributed systems that is not captured by the above spanner definition is the possibility of failures. We would like our spanner to be robust to failures, so that even if some nodes fail we still have a spanner of what remains. More formally, is an (vertex)fault tolerant spanner of if for every set with the spanner condition holds for , i.e.,
for all . If is instead an edge set then this gives a definition of an edgefaulttolerant spanner.
This notion of faulttolerant spanners was first introduced by Levcopoulos, Narasimhan, and Smid [LNS98] in the context of geometric spanners (the special case when the vertices are in Euclidean space and the distance between two points is the Euclidean distance), and has since been studied extensively in that setting [LNS98, Luk99, CZ04, NS07]. Note that in the geometric setting, for all , since faults do not change the underlying geometric distances.
In general graphs, though, may be extremely different from , making this definition more difficult to work with. The first results on faulttolerant graph spanners were by Chechik, Langberg, Peleg, and Roditty [CLPR10], who showed how to modify the ThorupZwick spanner [TZ05] to be fault tolerant with an additional cost of approximately : the number of edges in the fault tolerant spanner that they create is approximately (where hides polylogarithmic factors). Since [CLPR10] there has been a significant amount of work on improving the sparsity, particularly as a function of the number of faults (since we would like to protect against large numbers of faults but usually care most about small stretch values). First, Dinitz and Krauthgamer [DK11] improved the size to by giving a blackbox reduction to the traditional nonfault tolerant setting. Then Bodwin, Dinitz, Parter, and Vassilevska Williams [BDPW18] decreased this to , which they also showed was optimal (for vertex faults) as a function of and (i.e., the only nonoptimal dependence is the ). Unlike previous faulttolerant spanner constructions, this optimal construction was based off of a natural greedy algorithm (the natural generalization of the greedy algorithm of [ADD93]). An improved analysis of the same greedy algorithm was then given by Bodwin and Patel [BP19], who managed to show the fully optimal bound of .
Unlike the previous faulttolerant spanner constructions of [CLPR10, DK11] and the greedy nonfault tolerant algorithm of [ADD93], the greedy algorithm of [BDPW18, BP19] has a significant weakness: it takes exponential time. Obtaining the same (or similar) size bound in polynomial time was explicitly mentioned as an important open question in both [BDPW18] and [BP19].
1.1 Our Results and Techniques
In this paper we design a surprisingly simple algorithm to construct nearlyoptimal faulttolerant spanners in polynomial time, in both unweighted and weighted graphs.
Theorem 2.
There is a polynomial time algorithm which, given integers and and a (weighted) graph , constructs an fault tolerant spanner with at most edges in time (for vertex fault tolerance) or (for edge fault tolerance).
Note that while we are a factor of away from complete optimality (for vertex faults), this is truly optimal when the stretch is constant and, for nonconstant stretch values, is still significantly sparser than the analysis of the exponential time algorithm by [BDPW18] (which lost an exponential factor in ).
The main idea in our algorithm is to replace the exponentialtime subroutine used in the greedy algorithm of [BDPW18, BP19] with an appropriate polynomialtime approximation algorithm. More specifically, the main step of the exponential time greedy algorithm is to consider whether a given candidate edge is “already spanned” by the subgraph that has already been built. This means determining whether, for some candidate edge , there is a fault set with such that . If such a fault set exists then the algorithm adds to , and otherwise does not^{1}^{1}1Note that in the faultfree case this just means checking whether there is already a path of stretch at most between the endpoints, which is precisely the original greedy algorithm of [ADD93].. In both [BDPW18] and [BP19], the only method given to find such a set was to try all possible sets, giving running time that is exponential in and thus exponential in the size of the input.
Our main approach is to speed this up by designing a polynomialtime algorithm to replace this exponentialtime step. Unfortunately, the corresponding problem (known as LengthBounded Cut) is NPhard [BEH06], so we cannot hope to actually solve it efficiently. Instead, we design an approximation algorithm for LengthBounded Cut and use it instead. We end up with a fairly weak approximation (basically a approximation), and one which only holds in the unweighted case. But this turns out to be enough for the unweighted case: it intuitively allows us to (in polynomial time) build a faulttolerant spanner instead of an fault tolerant spanner, which changes the extra size cost from to . However, this is only intuition. The graph we end up creating is not in fact fault tolerant, and is not necessarily even a subgraph of the faulttolerant spanner that the true greedy algorithm would have built. So we cannot simply argue that our algorithm returns something with at most as many edges as the greedy faulttolerant greedy spanner. Instead, we need to analyze the size of our spanner from scratch. Fortunately, we can do this by simply following the proof strategy of [BP19] with only some minor modifications.
A natural approach to the weighted case would be to try to generalize this by creating an approximation for LengthBounded Cut in the weighted setting. Such an algorithm would certainly suffice, but unfortunately we do not know how to design any nontrivial approximation algorithm for LengthBounded Cut in the presence of weights. While this might appear to rule out using a similar technique, we show that special properties of the greedy algorithm allow us to essentially reduce to the unweighted setting! We use the weights to determine the order in which we consider edges, but for the rest of the algorithm we simply “pretend” to be in the unweighted setting. Since the size bound for the unweighted case worked for any ordering, that same size bound will apply to our spanner. And then we can use the fact that we considered edges in order of nondecreasing weights to argue that the subgraph we create is in fact an fault tolerant spanner even though we ignored the weights.
Distributed Settings
While the focus of this paper is on a centralized polynomialtime algorithm since the existence of such an algorithm was an explicit open question from [BDPW18] and [BP19], we complement this result with some simple algorithms in the standard LOCAL and CONGEST models of distributed computation.
In the LOCAL model, we can use standard network decompositions to find a clustering of the graph where the clusters have low diameter, every edge is in at least one cluster, and the clustering comes from partitions. Since in the LOCAL model we are allowed unbounded message sizes, this means that in time we can send the subgraph induced by each cluster to the cluster center (an arbitrary node in the cluster), who can then locally run the greedy algorithm on that cluster and then inform the nodes in the cluster about the edges that have been chosen. This will take only communication rounds (since clusters have diameter ) and will incur only an extra factor in the number of edges (since the clustering can be divided into partitions).
In the CONGEST model we cannot apply this approach (even though we could find a similar clustering) because we are not able to gather large induced subgraphs at the cluster centers (due to the bound on message sizes). Instead, we show that the older faulttolerant spanner construction of [DK11] can be combined with the standard (nonfaulttolerant) spanner algorithm in the CONGEST model due to Baswana and Sen [BS07] to give a faulttolerant spanner algorithm in CONGEST. This approach means that the size increases to (so we are a factor of away from the bounds of the polynomialtime greedy algorithm), but the number of rounds needed is quite small despite the limitation on message sizes ( rounds).
2 Notation and Preliminaries
We will be discussing graphs where and . Sometimes these graphs will also have a weight function . We will slightly abuse notation to let for all . For a (possibly weighted) graph , we will let denote the length of the shortest (lowestweight) path from to (if no such path exists then this length is ). For any , we let denote the subgraph of induced by . For let be , and for let be .
Definition 1.
Let be a (possibly weighted) graph. A subgraph of is an vertexfaulttolerant (VFT) spanner of if for all with and . A subgraph of is an edgefaulttolerant (EFT) spanner of if for all with .
Throughout this paper, for simplicity we will only discuss the vertex fault tolerant case since that is the more difficult one to prove upper bounds for. The proofs for the edge fault tolerant case are essentially identical, with only one step of the algorithm running slightly slower.
We first show an equivalent definition that will let us restrict which pairs of vertices we care about.
Lemma 3.
Let be a graph with weight function and let be a subgraph of . Then is an VFT spanner of if and only if for all with and such that and
Proof.
The only if direction is immediately implied by Definition 1, since for any with and such that and , we know from Definition 1 that .
For the if direction, let with and . Let be the shortest path in between and . If then , and thus . If , then we know that for all , and thus
Hence is an VFT spanner of . ∎
The original greedy algorithm for faulttolerant spanners was introduced and analyzed by [BDPW18], with an improved analysis by [BP19], and is given in Algorithm 1. The part of this algorithm which takes exponential time is the “if” condition, i.e., checking whether there is a fault set which hits all stretch paths. For edge fault tolerance, the algorithm is the same except that is an edge set.
3 Unweighted Graphs
In this section we design a polynomialtime algorithm for the special case of unweighted (or unitweighted) graphs. We begin by designing a simple approximation algorithm for the LengthBounded Cut problem, and then show that this algorithm can be plugged into the greedy algorithm with only a small loss.
3.1 LengthBounded Cut
In order to design a polynomialtime variant of the greedy algorithm, we want to replace the “if” condition by something that can be computed in polynomial time. While there are many possibilities, there are two obvious approaches: we could try to compute the the maximum such that there is a fault set of size which hits all hop paths, or we could try to compute the minimum such that there is a fault set of size which hits all hop paths. It turns out that this second approach is more fruitful.
Consider the following problem, known as the LengthBounded Cut problem [BEH06]. The input is an unweighted graph with and , vertices (known as the terminals), and a positive integer . The goal is to return the set of minimum cardinality such that
Theorem 4.
There is a approximation algorithm for LengthBounded Cut which runs in time.
Proof.
Recall that the classical BellmanFord algorithm has the property that each iteration takes time, and after iterations it has computed the shortest hop path from the source to the destination. So we can use BellmanFord to check whether there is a path with at most hops from to in time.
This gives the following natural algorithm, which is essentially the standard “frequency” approximation of Set Cover (or Hitting Set). Initialize . While there exists a path of length at most from to in , add all nodes of (other than and ) to . Obviously when this algorithm terminates, is a feasible lengthbounded cut. And it clearly runs in at most time, since each iteration of the algorithm takes time and there can be at most iterations (since in each iteration we add at least one more node to ).
To bound the approximation ratio, let denote the optimal lengthbounded cut. Then for every path which our algorithm considers (and adds to ), it must be the case that since must hit all paths of length at most . Let be the set of all paths considered by the algorithm (where we have removed from the paths), and notice that they are all nodedisjoint, have at most nodes in them, and . Hence
where for the last inequality we used that the paths in are nodedisjoint. ∎
To handle edge faulttolerance, we need to slightly change the definition of LengthBounded Cut to return an edge set rather than a vertex set. The same algorithm works (where in each iteration we add all edges in the path to rather than all vertices), but it becomes a approximation rather than a approximation. It also runs in time rather than , since we might have to run BellmanFord times rather than times. This is what results in the slightly worse time bound for our EFT spanner construction compared to our VFT spanner construction.
3.2 Modified Greedy
Let be an undirected unweighted graph. In order to construct a fault tolerant spanner of we consider the following natural extension of the greedy algorithm for constructing spanners. For an EFT spanner algorithm, we simply use the edgebased version of LengthBounded Cut and use the threshold rather than .
Theorem 5.
The running time of Algorithm 2 is at most for vertex fault tolerance and for edge fault tolerance.
Proof.
We run our LengthBounded Cut approximation algorithm once for each edge in . Thus the running time of Algorithm 1 is for VFT and for EFT. ∎
We now prove that this algorithm does indeed return a valid solution, despite the use of an approximation algorithm to determine whether or not to add an edge (we prove this only for VFT for simplicity, but the proof for EFT is the same).
Theorem 6.
Algorithm 2 returns an VFT spanner.
Proof.
Let be an arbitrary fault set with and with . By Lemma 3, we just need to show that (since is unweighted) in order to prove the theorem. Clearly this is true if . If , then when the algorithm considered it must have been the case that . Since was the value returned by the algorithm of Theorem 4, we know from the approximation guarantee of Theorem 4 that the true minimum lengthbounded cut on (for with length bound ) has at least nodes in it. Thus is not a lengthbounded cut in for with length bound , and so . ∎
Now it remains only to prove the size of the returned spanner. To do this, a natural approach would be to argue that the spanner it returns is a subgraph of the greedy VFT spanner, since it seems like whenever our modified algorithm requires us to add an edge it has found a cut certifying that the greedy fault tolerant spanner would also have had to add that edge. Unfortunately, this is not true since the modified algorithm might not add some edges that the true greedy algorithm would have added, and thus later on our algorithm might have to actually add some edges that the true greedy algorithm would not have had to add.
The next natural approach would be to try to use the analysis of [BP19] as a black box. Unfortunately we cannot do this either, since the lemmas they use are specific to the true greedy algorithm rather than our modification. However, it is straightforward to modify their analysis so that it continues to hold for our modified algorithm, with only an additional loss of . We do this here for completeness. As in [BP19], we start with the definition of a blocking set, and then give two lemmas using this definition. And also as in [BDPW18, BP19], we only prove this for VFT, as the proof for EFT is essentially identical.
Definition 2 ([Bp19]).
For any graph , we define to be a blocking set of if for all , we have and for any cycle in with , there exists such that .
Lemma 7.
Any graph returned by Algorithm 2 with parameters has a blocking set of size at most .
It was shown in [BP19] that the graph returned by the standard VFT greedy algorithm with parameters has a ()blocking set of size at most .^{2}^{2}2In [BP19] the parameter “” is used to denote the stretch, while for us the stretch is , and thus there are slight constant factor differences between the statements as written in [BP19] and our interpretation of their statements. But our statements about [BP19] are correct under this change of variables. So our modified algorithm satisfies the same lemma up to a factor of . The proof is almost identical in our case; we essentially replace all instances of in their proof with .
Proof of Lemma 7.
Let be some edge in , and let be the subgraph maintained by the algorithm just before is added to (so is a subset of the final ). Since was added by Algorithm 2, there is some set with such that .
Now we can define the blocking set: let .
Since for all , we immediately get that as claimed. So we now need to show that is a blocking set. To see this, let be any cycle with at most vertices in , and let be the last edge of this cycle to be added to . Let be the subgraph of built by the algorithm just before is added. Then is a path in of length at most , and thus there is some that is in . Thus . ∎
Now we know that the spanner returned by Algorithm 2 has a small blocking set. The next lemma implies that any such graph must have a dense but highgirth subgraph.
Lemma 8.
Let be any graph on nodes and edges (with ) that has a blocking set of size at most . Then has a subgraph on nodes and edges that has girth greater than .
Proof.
Let denote the induced subgraph of on a uniformly random subset of exactly nodes. Let , and let denote the graph obtained by removing every edge contained in any pair in . The graph will be the one we analyze.
The easiest property to analyze is the number of nodes in : there are precisely vertices in , which is as claimed.
The next easiest property of to prove is the girth. Let be a cycle in with at most nodes. is either in or it is not. If it is not in then some vertex in is not in , and thus is not in . On the other hand, if is in then by the definition of there is some edge so that , and also , and thus does not exist in .
To analyze , we start with the following observations.

Each remains in if . This happens with probability
Now we can use these observations to compute the expected size of :
Note that the bounds on and on the girth of are deterministic. So there is some subgraph which has those bounds and where the number of edges is at least the expectation, proving the lemma. ∎
This lemma allows us to prove the size bound.
Theorem 9.
The subgraph returned by Algorithm 2 has at most edges.
Proof.
If then the theorem is trivially true. Otherwise, by Lemmas 7 and 8 we know that has a subgraph of girth larger than on nodes and edges. But it has long been known that any graph with vertices and girth larger than must have at most edges (this is the key fact used in the original nonfault tolerant greedy algorithm analysis [ADD93]), and hence . Therefore we have
4 Weighted Graphs
We now show that we can use algorithm we designed for the weighted setting even in the presence of weights. Our algorithm is very simple: we order the edges in nondecreasing weight order, but then run the unweighted algorithm on the edges in this order. We give this algorithm more formally as Algorithm 3. Again, changing to edge fault tolerance is straightforward: we just use the edge version of LengthBounded Cut and change to . So we prove this only for vertex fault tolerance for simplicity.
Theorem 10.
Algorithm 3 returns an VFT (EFT) spanner with at most edges in time at most ().
Proof.
The running time is directly from Theorem 5, since the only additional step in the algorithm is sorting the edges by weight, which takes only additional time. The size also follows directly from Theorem 9, since Algorithm 3 is just a particular instantiation of Algorithm 2 where the ordering (which is unspecified in Algorithm 2) is determined by the weights. In other words, Theorem 9 holds for an arbitrary order, so it certainly holds for the weight ordering.
The more interesting part of this theorem is correctness: why does this algorithm return an VFT spanner despite ignoring weights? Let be an arbitrary fault set with and with and . By Lemma 3, we just need to show that in order to prove the theorem. Clearly this is true if . So suppose that . Then when the algorithm considered , it must have been the case that . Then from the approximation bound of Theorem 4 we know that the minimum lengthbounded cut in (unweighted) for with (unweighted) length bound has size at least , and thus is not such a cut. Thus at the time the algorithm was considering , there was some path between and in with at most edges. But since we considered edges in order of nondecreasing weight, every edge in has weight at most . Thus
as required. ∎
5 Distributed Algorithms
In this section we give efficient algorithms in two standard distributed models: the LOCAL model and the CONGEST model [Pel00]. Recall that in both models we assume communication happens in synchronous rounds, and our goal is to minimize the number of rounds needed. In the LOCAL model each node can send an arbitrary message on each incident edge in each round, while in the CONGEST model these messages must have size at most bits (or words, so we can send a constant number of node IDs and weights in each message). Note that both models allow unlimited computation at each node, and hence the difficulty with applying the greedy algorithm is not the exponential running time, but its inherently sequential nature.
5.1 Local
In the LOCAL model we will be able to implement the greedy algorithm at only a small extra cost in the size of the spanner. Our approach is simple: we use standard network decompositions to decompose the graph into clusters, run the greedy algorithm in each cluster, and then take the union of the spanner for each cluster.
The following theorem is a simple corollary of the construction of “padded decompositions” given explicitly in previous work on faulttolerant spanners
[DK11]. It also appears implicitly in various forms in [LS93, Bar96, MPX13, MPVX15] (among others). In what follows, the hop diameter of a cluster refers to its unweighted diameter.Theorem 11.
There is an algorithm in the LOCAL model which runs in rounds and constructs such that:

Each is a partition of , with each part of the partition referred to as a cluster. Let be the collection of all clusters of all partitions.

Each cluster has hop diameter at most and contains some special node known as the cluster center.

(there are partitions).

With high probability ( for any constant ) for every edge there is a cluster such that .
With this tool, it is easy to describe our algorithm. First we use Theorem 11 to construct the partitions. Then in each cluster we gather at the cluster center the entire subgraph induced by that cluster. Each cluster center uses the greedy algorithm (Algorithm 1) on to construct an VFT spanner of , and then sends out the selected edges to the nodes in . Let be the final subgraph created (the union of the edges of each )
Theorem 12.
With high probability, is an VFT spanner of with at most edges and the algorithm terminates in rounds.
5.2 Congest
We unfortunately cannot use the approach that we used in the LOCAL model in the CONGEST model, since we cannot efficiently gather the entire topology of a cluster at a single node. We will instead use the faulttolerant spanner of Dinitz and Krauthgamer [DK11], rather than the greedy algorithm, and combine it with the nonfault tolerant spanner of [BS07] which can be efficiently constructed in CONGEST. This approach means that, unlike in the centralized setting or the LOCAL model, we will not be able to get sizeoptimal faulttolerant spanners.
The algorithm of [DK11] works as follows (in the traditional centralized model). Suppose that we have some algorithm which constructs a spanner with at most edges on any graph with nodes. There are iterations, and in every iteration each node chooses to participate independently with probability . For each , let be the vertices who participate and let be the subgraph of induced by them. We let be the spanner constructed by on . Then we return the union of all .
The main theorem that [DK11] proved about this is the following.
Theorem 13 ([Dk11]).
This algorithm returns an VFT spanner of with edges with high probability.
Note that when , this results in an VFT spanner with at most , which is precisely the bound from [DK11].
Since the algorithm of [DK11] uses an arbitrary nonfault tolerant spanner algorithm , by using a distributed spanner algorithm for we naturally end up with a distributed faulttolerant spanner algorithm. In particular, we will combine the algorithm of [DK11] with the following algorithm due to Baswana and Sen [BS07].
Theorem 14 ([Bs07]).
There is an algorithm that computes a spanner with at most edges of any weighted graph in rounds in the CONGEST model.
Combining Theorems 13 and 14 immediately gives an algorithm in CONGEST that returns an VFT spanner of size at most that runs in at most rounds (with high probability). We can just run each iteration of the DinitzKrauthgamer algorithm [DK11] in series, and in each iteration we use the BaswanaSen algorithm [BS07]. Since there are iterations, and BaswanaSen takes rounds, this gives a total round complexity of .
We can improve on this bound by taking advantage of the fact that each iteration of DinitzKrauthgamer runs on a relatively small graph (approximately nodes), so we can run some of these iterations in parallel.
Theorem 15.
There is an algorithm that computes an VFT spanner of with edges of any weighted graph and which runs in rounds in the CONGEST model (all with high probability).
Proof.
In the first phase of the algorithm each vertex randomly selects iterations out of the total in which to participate. Then each vertex sends its chosen iterations to all of its neighbors. Identifying these iterations take bits, and thus rounds in CONGEST.
After this has completed we enter the second phase of the algorithm, and now every node knows which iterations it is participating in and which iterations each of its neighbors is participating in. With high probability, for every edge there are at most iterations in which both endpoints participate. Thus if we try to run all iterations of BaswanaSen (Theorem 14) in parallel, we have “congestion” of on each edge (at each time step) since there could be up to that many iterations in which a message is supposed to be sent along that edge at that time. Thus we can simply use time steps for each time step of BaswanaSen and can simulate all iterations of the DinitzKrauthgamer algorithm (note that each BaswanaSen message needs to have a tag added to it with the iteration number, but since that takes at most bits it fits within the required message size). Hence the total running time of this second phase is at most .
6 Conclusion and Future Work
In this paper we designed an algorithm to compute nearlyoptimal faulttolerant spanners in polynomial time, answering a question posed by [BDPW18, BP19]. We also gave an optimal construction in the LOCAL model which runs in rounds, and an efficient algorithm in the CONGEST model that constructs faulttolerant spanners which have the same size as in [DK11] rather than the optimal size.
There are many interesting open questions remaining about efficient algorithms for faulttolerant spanners, as well as about the extremal properties of these spanners. Most obviously, the size we achieve is a factor of away from the optimal size, due to our use of an approximation for LengthBounded Cut. Can this be removed, either by giving a better approximation for LengthBounded Cut or through some other construction? While is somewhat small since spanners tend to be most useful for constant stretch (and never have stretch larger than ), it would still be nice to get fully optimal size in polynomial time. Similarly, our distributed constructions are extremely simple, and there is no reason to think that we actually need rounds in LOCAL or that we cannot get optimal size faulttolerant spanners in CONGEST. It would be interesting to design better distributed and parallel algorithms for these objects, particularly since the greedy algorithm (the only sizeoptimal algorithm we know) tends to be difficult to parallelize.
From a structural point of view, we reiterate one of the main open questions from [BDPW18] and [BP19]: understanding the optimal bounds for edgefaulttolerant spanners. The best upper bound we have is the same that we have for the vertex case, while the best lower bound is (from [BDPW18]). What is the correct bound?
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