A New Algorithm for Decremental Single-Source Shortest Paths with Applications to Vertex-Capacitated Flow and Cut Problems
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We study the vertex-decremental Single-Source Shortest Paths (SSSP) problem: given an undirected graph G=(V,E) with lengths ℓ(e)≥ 1 on its edges and a source vertex s, we need to support (approximate) shortest-path queries in G, as G undergoes vertex deletions. In a shortest-path query, given a vertex v, we need to return a path connecting s to v, whose length is at most (1+ϵ) times the length of the shortest such path, where ϵ is a given accuracy parameter. The problem has many applications, for example to flow and cut problems in vertex-capacitated graphs. Our main result is a randomized algorithm for vertex-decremental SSSP with total expected update time O(n^2+o(1) L), that responds to each shortest-path query in O(n L) time in expectation, returning a (1+ϵ)-approximate shortest path. The algorithm works against an adaptive adversary. The main technical ingredient of our algorithm is an Õ(|E(G)|+ n^1+o(1))-time algorithm to compute a core decomposition of a given dense graph G, which allows us to compute short paths between pairs of query vertices in G efficiently. We believe that this core decomposition algorithm may be of independent interest. We use our result for vertex-decremental SSSP to obtain (1+ϵ)-approximation algorithms for maximum s-t flow and minimum s-t cut in vertex-capacitated graphs, in expected time n^2+o(1), and an O(^4n)-approximation algorithm for the vertex version of the sparsest cut problem with expected running time n^2+o(1). These results improve upon the previous best known results for these problems in the regime where m= ω(n^1.5 + o(1)).
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