Fast Computation of Small Cuts via Cycle Space Sampling
We describe a new sampling-based method to determine cuts in an undirected graph. For a graph (V, E), its cycle space is the family of all subsets of E that have even degree at each vertex. We prove that with high probability, sampling the cycle space identifies the cuts of a graph. This leads to simple new linear-time sequential algorithms for finding all cut edges and cut pairs (a set of 2 edges that form a cut) of a graph. In the model of distributed computing in a graph G = (V, E) with O(log vertical bar V vertical bar)-bit messages, our approach yields faster algorithms for several problems. The diameter of G is denoted by D, and the maximum degree by Delta. We obtain simple O(D)-time distributed algorithms to find all cut edges, 2-edge-connected components, and cut pairs, matching or improving upon previous time bounds. Under natural conditions these new algorithms are universally optimal-that is, a Omega(D)-time lower bound holds on every graph. We obtain a O(D + Delta/log vertical bar V vertical bar)-time distributed algorithm for finding cut vertices; this is faster than the best previous algorithm when Delta, D = O(root vertical bar V vertical bar). A simple extension of our work yields the first distributed algorithm with sub-linear time for 3-edge-connected components. The basic distributed algorithms are Monte Carlo, but they can be made Las Vegas without increasing the asymptotic complexity. In the model of parallel computing on the EREW PRAM, our approach yields a simple algorithm with optimal time complexity O(log V) for finding cut pairs and 3-edge-connected components.
distributed algorithms graph connectivity linear algebra parallel algorithms randomized algorithms universal optimality