Dynamic algorithms for k-center on graphs
This solves a fundamental clustering problem for dynamic graph applications, offering efficient algorithms where prior work only handled point sets in metric spaces.
The paper tackles the k-center problem on dynamic graphs by providing the first efficient algorithms for edge updates, achieving deterministic decremental (2+ε)-approximation and randomized incremental (4+ε)-approximation with amortized update time kn^{o(1)}, and a fully dynamic (2+ε)-approximation algorithm with worst-case update time within a factor k of state-of-the-art single-source shortest paths algorithms.
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 its 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 point sets in arbitrary metric spaces. 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 fully dynamic $(1+ε)$-approximation single-source shortest paths algorithm 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.