Near-Optimal Deterministic Network Decomposition and Ruling Set, and Improved MIS

This paper improves and in two cases nearly settles, up to logarithmically lower-order factors, the deterministic complexity of some of the most central problems in distributed graph algorithms, which have been studied for over three decades: Near-Optimal Network Decomposition: We present a deterministic distributed algorithm that computes a network decomposition in approximately O(log^2 n) rounds, with O(log n) diameter and O(log n) colors. This round complexity is near-optimal in the following sense: even given an ideal network decomposition, using it (in the standard way) requires round complexity equal to the product of diameter and number of colors, which is known to be approximately Omega(log^2 n). This near-optimality is remarkable, considering the rarity of optimal deterministic distributed algorithms and that for network decomposition, the first polylogarithmic-round algorithm was achieved only recently, by Rozhon and Ghaffari [STOC 2020], after three decades. Near-Optimal Ruling Set: We present a deterministic distributed algorithm that computes an O(log log n) ruling set in approximately O(log n) rounds. This is an exponential improvement over the O(log n) ruling set of Awerbuch, Goldberg, Luby, and Plotkin [FOCS 1989], while almost matching their O(log n) round complexity. Our result's round complexity nearly matches the approximately Omega(log n) lower bound established by Balliu, Brandt, Kuhn, and Olivetti [STOC 2022], which applies to any poly(log log n) ruling set. Improved Maximal Independent Set (MIS): We present a deterministic distributed algorithm for computing an MIS in approximately O(log^(5/3) n) rounds. This improves upon the approximately O(log^2 n) complexity achieved by Ghaffari and Grunau [STOC 2023] and breaks the log-squared barrier necessary for any method based on network decomposition.