Degree-greedy algorithms on large random graphs

Abstract

Computing the size of maximum independent sets is an NP-hard problem for fixed graphs. Characterizing and designing efficient algorithms to compute (or approximate) this independence number for random graphs are notoriously difficult and still largely open issues. In this paper, we show that a low complexity degree-greedy exploration is actually asymptotically optimal on a large class of sparse random graphs. Encouraged by this result, we present and study two variants of sequential exploration algorithms: static and dynamic degree-aware explorations. We derive hydrodynamic limits for both of them, which in turn allow us to compute the size of the resulting independent set. Whereas the former is simpler to compute, the latter may be used to arbitrarily approximate the degree-greedy algorithm. Both can be implemented in a distributed manner. The corresponding hydrodynamic limits constitute an efficient method to compute or bound the independence number for a large class of sparse random graphs. As an application, we then show how our method may be used to compute (or approximate) the capacity of a large 802.11-based wireless network.