Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better
Data Structures and Algorithms
2024-04-26 v1 Machine Learning
Social and Information Networks
Abstract
Cluster deletion is an NP-hard graph clustering objective with applications in computational biology and social network analysis, where the goal is to delete a minimum number of edges to partition a graph into cliques. We first provide a tighter analysis of two previous approximation algorithms, improving their approximation guarantees from 4 to 3. Moreover, we show that both algorithms can be derandomized in a surprisingly simple way, by greedily taking a vertex of maximum degree in an auxiliary graph and forming a cluster around it. One of these algorithms relies on solving a linear program. Our final contribution is to design a new and purely combinatorial approach for doing so that is far more scalable in theory and practice.
Cite
@article{arxiv.2404.16131,
title = {Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better},
author = {Vicente Balmaseda and Ying Xu and Yixin Cao and Nate Veldt},
journal= {arXiv preprint arXiv:2404.16131},
year = {2024}
}