A deterministic almost-tight distributed algorithm for approximating single-source shortest paths

We present a deterministic (1+𝑜(1))-approximation (𝑛1/2+𝑜(1)+𝐷1+𝑜(1))-time algorithm for solving the single-source shortest paths problem on distributed weighted networks (the \sf CONGEST model); here 𝑛 is the number of nodes in the network, 𝐷 is its (hop) diameter, and edge weights are positive integers from 1 to poly(𝑛). This is the first nontrivial deterministic algorithm for this problem. It also improves (i) the running time of the randomized (1+𝑜(1))-approximation 𝑂̃ (𝑛√𝐷1/4+𝐷)-time algorithm of Nanongkai [in Proceedings of STOC, 2014, pp. 565--573] by a factor of as large as 𝑛1/8, and (ii) the 𝑂(𝜖−1log𝜖−1)-approximation factor of Lenzen and Patt-Shamir's 𝑂̃ (𝑛1/2+𝜖+𝐷)-time algorithm [in Proceedings of STOC, 2013, pp. 381--390] within the same running time. (Throughout, we use 𝑂̃ (⋅) to hide polylogarithmic factors in 𝑛.) Our running time matches the known time lower bound of Ω(𝑛/log𝑛‾‾‾‾‾‾‾√+𝐷) [M. Elkin, SIAM J. Comput., 36 (2006), pp. 433--456], thus essentially settling the status of this problem which was raised at least a decade ago [M. Elkin, SIGACT News, 35 (2004), pp. 40--57]. It also implies a (2+𝑜(1))-approximation (𝑛1/2+𝑜(1)+𝐷1+𝑜(1))-time algorithm for approximating a network's weighted diameter which almost matches the lower bound by Holzer and Pinsker [in Proceedings of OPODIS, 2015, Schloss Dagstuhl. Leibniz-Zent. Inform., Wadern, Germany, 2016, 6]. In achieving this result, we develop two techniques which might be of independent interest and useful in other settings: (i) a deterministic process that replaces the “hitting set argument” commonly used for shortest paths computation in various settings, and (ii) a simple, deterministic construction of an (𝑛𝑜(1),𝑜(1))-hop set of size 𝑛1+𝑜(1). We combine these techniques with many distributed algorithmic techniques, some of which are from problems that are not directly related to shortest paths, e.g., ruling sets [A. V. Goldberg, S. A. Plotkin, and G. E. Shannon, SIAM J. Discrete Math., 1 (1988), pp. 434--446], source detection [C. Lenzen and D. Peleg, in Proceedings of PODC, 2013, pp. 375--382], and partial distance estimation [C. Lenzen and B. Patt-Shamir, in Proceedings of PODC, 2015, pp. 153--162]. Our hop set construction also leads to single-source shortest paths algorithms in two other settings: (i) a (1+𝑜(1))-approximation 𝑛𝑜(1)-time algorithm on congested cliques, and (ii) a (1+𝑜(1))-approximation 𝑛𝑜(1)-pass 𝑛1+𝑜(1)-space streaming algorithm. The first result answers an open problem in [D. Nanongkai, in Proceedings of STOC, 2014, pp. 565--573]. The second result partially answers an open problem raised by McGregor in 2006 [List of Open Problems in Sublinear Algorithms: Problem 14].