Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models
CoRR(2024)
摘要
Given a graph with positive and negative edge labels, the correlation
clustering problem aims to cluster the nodes so to minimize the total number of
between-cluster positive and within-cluster negative edges. This problem has
many applications in data mining, particularly in unsupervised learning.
Inspired by the prevalence of large graphs and constantly changing data in
modern applications, we study correlation clustering in dynamic, parallel
(MPC), and local computation (LCA) settings. We design an approach that
improves state-of-the-art runtime complexities in all these settings. In
particular, we provide the first fully dynamic algorithm that runs in an
expected amortized constant time, without any dependence on the graph size.
Moreover, our algorithm essentially matches the approximation guarantee of the
celebrated Pivot algorithm.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要