Dynamic algorithms for k-center on graphs
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In this paper we give the first efficient algorithms for the k-center problem on dynamic graphs undergoing edge updates. In this problem, the goal is to partition the input into k sets by choosing k centers such that the maximum distance from any data point to the closest center is minimized. It is known that it is NP-hard to get a better than 2 approximation for this problem. While in many applications the input may naturally be modeled as a graph, all prior works on k-center problem in dynamic settings are on metrics. In this paper, we give a deterministic decremental (2+ϵ)-approximation algorithm and a randomized incremental (4+ϵ)-approximation algorithm, both with amortized update time kn^o(1) for weighted graphs. Moreover, we show a reduction that leads to a fully dynamic (2+ϵ)-approximation algorithm for the k-center problem, with worst-case update time that is within a factor k of the state-of-the-art upper bound for maintaining (1+ϵ)-approximate single-source distances in graphs. Matching this bound is a natural goalpost because the approximate distances of each vertex to its center can be used to maintain a (2+ϵ)-approximation of the graph diameter and the fastest known algorithms for such a diameter approximation also rely on maintaining approximate single-source distances.
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