Vertex Fault Tolerant Additive Spanners

A {\em fault-tolerant} structure for a network is required to continue functioning following the failure of some of the network's edges or vertices. In this paper, we address the problem of designing a {\em fault-tolerant} additive spanner, namely, a subgraph $H$ of the network $G$ such that subsequent to the failure of a single vertex, the surviving part of $H$ still contains an \emph{additive} spanner for (the surviving part of) $G$, satisfying $dist(s,t,H\setminus \{v\}) \leq dist(s,t,G\setminus \{v\})+\beta$ for every $s,t,v \in V$. Recently, the problem of constructing fault-tolerant additive spanners resilient to the failure of up to $f$ \emph{edges} has been considered by Braunschvig et. al. The problem of handling \emph{vertex} failures was left open therein. In this paper we develop new techniques for constructing additive FT-spanners overcoming the failure of a single vertex in the graph. Our first result is an FT-spanner with additive stretch $2$ and $\widetilde{O}(n^{5/3})$ edges. Our second result is an FT-spanner with additive stretch $6$ and $\widetilde{O}(n^{3/2})$ edges. The construction algorithm consists of two main components: (a) constructing an FT-clustering graph and (b) applying a modified path-buying procedure suitably adopted to failure prone settings. Finally, we also describe two constructions for {\em fault-tolerant multi-source additive spanners}, aiming to guarantee a bounded additive stretch following a vertex failure, for every pair of vertices in $S \times V$ for a given subset of sources $S\subseteq V$. The additive stretch bounds of our constructions are 4 and 8 (using a different number of edges)

Sparse Fault-Tolerant BFS Trees

This paper addresses the problem of designing a sparse {\em fault-tolerant} BFS tree, or {\em FT-BFS tree} for short, namely, a sparse subgraph $T$ of the given network $G$ such that subsequent to the failure of a single edge or vertex, the surviving part $T'$ of $T$ still contains a BFS spanning tree for (the surviving part of) $G$. Our main results are as follows. We present an algorithm that for every $n$-vertex graph $G$ and source node $s$ constructs a (single edge failure) FT-BFS tree rooted at $s$ with O(n \cdot \min\{\Depth(s), \sqrt{n}\}) edges, where \Depth(s) is the depth of the BFS tree rooted at $s$. This result is complemented by a matching lower bound, showing that there exist $n$-vertex graphs with a source node $s$ for which any edge (or vertex) FT-BFS tree rooted at $s$ has $\Omega(n^{3/2})$ edges. We then consider {\em fault-tolerant multi-source BFS trees}, or {\em FT-MBFS trees} for short, aiming to provide (following a failure) a BFS tree rooted at each source $s\in S$ for some subset of sources $S\subseteq V$. Again, tight bounds are provided, showing that there exists a poly-time algorithm that for every $n$-vertex graph and source set $S \subseteq V$ of size $\sigma$ constructs a (single failure) FT-MBFS tree $T^*(S)$ from each source $s_i \in S$, with $O(\sqrt{\sigma} \cdot n^{3/2})$ edges, and on the other hand there exist $n$-vertex graphs with source sets $S \subseteq V$ of cardinality $\sigma$, on which any FT-MBFS tree from $S$ has $\Omega(\sqrt{\sigma}\cdot n^{3/2})$ edges. Finally, we propose an $O(\log n)$ approximation algorithm for constructing FT-BFS and FT-MBFS structures. The latter is complemented by a hardness result stating that there exists no $\Omega(\log n)$ approximation algorithm for these problems under standard complexity assumptions

Light Spanners

A $t$-spanner of a weighted undirected graph $G=(V,E)$, is a subgraph $H$ such that $d_H(u,v)\le t\cdot d_G(u,v)$ for all $u,v\in V$. The sparseness of the spanner can be measured by its size (the number of edges) and weight (the sum of all edge weights), both being important measures of the spanner's quality -- in this work we focus on the latter. Specifically, it is shown that for any parameters $k\ge 1$ and $\epsilon>0$, any weighted graph $G$ on $n$ vertices admits a $(2k-1)\cdot(1+\epsilon)$-stretch spanner of weight at most $w(MST(G))\cdot O_\epsilon(kn^{1/k}/\log k)$, where $w(MST(G))$ is the weight of a minimum spanning tree of $G$. Our result is obtained via a novel analysis of the classic greedy algorithm, and improves previous work by a factor of $O(\log k)$.Comment: 10 pages, 1 figure, to appear in ICALP 201

Improved algorithms for all-pairs approximate shortest paths in weighted graphs

The all-pairs approximate shortest-paths problem is an interesting variant of the classical all-pairs shortest-paths problem in graphs. The problem aims at building a data-structure for a given graph with the following two features. Firstly, for any two vertices, it should report an {\emph{approximate}} shortest path between them, that is, a path which is longer than the shortest path by some {\emph{small}} factor. Secondly, the data-structure should require less preprocessing time (strictly sub-cubic) and occupy optimal space (sub-quadratic), at the cost of this approximation. In this paper, we present algorithms for computing all-pairs approximate shortest paths in a weighted undirected graph. These algorithms significantly improve the existing results for this problem

Approximate distance oracle for unweighted graphs in Õ(n²) time

Let G(V, E) be an undirected weighted graph with |V| = n, |E| = m. Recently Thorup and Zwick introduced a remarkable data-structure that stores all pairs approximate distance information implicitly in o(n2) space, and yet answers any approximate distance query in constant time. They named this data-structure approximate distance oracle because of this feature. Given an integer k < 1, a (2k-1)-approximate distance oracle requires O(kn1+1/k) space and answers a (2k-1)-approximate distance query in O(k) time. Thorup and Zwick showed that a (2k - 1)-approximate distance oracle can be computed in O(kmn1/k) time, and posed the following question : Can (2k - 1)-approximate distance oracle be computed in Õ(n2) time? In this paper, we answer their question in affirmative for unweighted graphs. We present an algorithm that computes (2k -1)-approximate distance oracle for a given unweighted graph in Õ(n2) time. One of the new ideas used in the improved algorithm also leads to the first linear time algorithm for computing an optimal size (2, 1)-spanner of an unweighted graph

All-pairs nearly 2-approximate shortest paths in O(n2polylogn)O(n^2 \mathrm polylog n) time

Let $G=(V,E)$ be an unweighted undirected graph on $n$ vertices. Let $\delta(u,v)$ denote the distance between vertices u,v\inV. An algorithm is said to compute all-pairs $t$-approximate shortest -paths/distances, for some $t\ge 1$, if for each pair of vertices $u,v\in V$, the path/distance reported by the algorithm is not longer/greater than $t\delta(u,v)$.\\ This paper presents two randomized algorithms for computing all-pairs nearly 2-approximate shortest distances. The first algorithm takes expected $O(m^{2/3}n\log n + n^2)$ time, and for any $u,v\in V$ reports distance no greater than $2\delta(u,v)+1$. Our second algorithm requires expected $O(n^2\log^{3/2} n)$ time, and for any $u,v\in V$, reports distance bounded by $2\delta(u,v) + 3$.\\ This paper also presents the first expected $O(n^2)$ time algorithm to compute all-pairs 3-approximate distances