Brief Announcement: Improved Distributed Approximations for Maximum-Weight Independent Set

Abstract

We present improved algorithms for approximating maximum-weight independent set (MaxIS) in the CONGEST model. Given an input graph, let n and Δ be the number of nodes and maximum degree, respectively, and let MIS(n, Δ) be the running time of finding a maximal independent set (MIS) in the CONGEST model. Bar-Yehuda et al. [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a Δ-approximation for MaxIS in O(MIS(n, Δ) log W) rounds, where W is the maximum weight of a node in the graph, which can be as high as poly(n). Whether their algorithm is deterministic or randomized depends on the MIS algorithm that is used as a black-box. Our results: (1) A deterministic O(MIS(n, Δ)/∈)-round algorithm that finds a (1 + ∈)Δ-approximation for MaxIS in the CONGEST model. (2) A randomized (poly(log log n)/∈)-round algorithm that finds, with high probability, a (1 + ∈)Δ-approximation for MaxIS in the CONGEST model. That is, by sacrificing only a tiny fraction of the approximation guarantee, we achieve an exponential speed-up in the running time over the previous best known result.