Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better

arXiv:2404.16131v1 Announce Type: cross
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 …

abstract algorithms analysis applications approximation arxiv biology cluster clustering computational computational biology cs.ds cs.lg cs.si faster graph improving minimum network np-hard social type