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SPARSE-PIVOT: Dynamic correlation clustering for node insertions

SPARSE-PIVOT: Dynamic correlation clustering for node insertions

来源:Arxiv_logoArxiv
英文摘要

We present a new Correlation Clustering algorithm for a dynamic setting where nodes are added one at a time. In this model, proposed by Cohen-Addad, Lattanzi, Maggiori, and Parotsidis (ICML 2024), the algorithm uses database queries to access the input graph and updates the clustering as each new node is added. Our algorithm has the amortized update time of $O_ε(\log^{O(1)}(n))$. Its approximation factor is $20+\varepsilon$, which is a substantial improvement over the approximation factor of the algorithm by Cohen-Addad et al. We complement our theoretical findings by empirically evaluating the approximation guarantee of our algorithm. The results show that it outperforms the algorithm by Cohen-Addad et al.~in practice.

Mina Dalirrooyfard、Konstantin Makarychev、Slobodan Mitrović

计算技术、计算机技术

Mina Dalirrooyfard,Konstantin Makarychev,Slobodan Mitrović.SPARSE-PIVOT: Dynamic correlation clustering for node insertions[EB/OL].(2025-07-02)[2025-07-23].https://arxiv.org/abs/2507.01830.点此复制

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