坏三角形横截的简单算法及其在相关聚类中的应用
Simple Algorithms for Bad Triangle Transversals with Applications to Correlation Clustering
AI总结:
本文提出坏三角形横截问题(BTT)的多个2-近似算法,证明其在完全图上NP难近似到优于2137/2136因子,并给出BTT与相关聚类(CC)最优值在完全图上相差不超过3/2因子的枢轴变换过程。
摘要:
坏三角形横截(BTT)问题要求从给定的符号图中移除最小的边集,使得结果图没有坏三角形。这里,坏三角形是恰好有一条负边的三角形。本文提出了BTT的几个2-近似算法。在难度方面,我们证明在完全图上,BTT是NP难的,且近似因子优于$\frac{2137}{2136}$。我们的归约也适用于相关聚类(CC)、簇删除问题(CD)和最小强三元闭包问题(MinSTC)。最后,我们通过描述一个将横截转化为簇的枢轴过程,证明了在完全图上BTT和CC的最优值相差不超过3/2因子。
英文摘要:
The Bad Triangle Transversal (BTT) problem asks for the smallest set of edges that need to be removed from a given signed graph, so that the resulting graph does not have a bad triangle. Here, a bad triangle is a triangle with exactly one negative edge. Several 2-approximations for BTT are proposed in this paper. On the hardness side, we show that BTT is NP-hard to approximate with factor better than $\frac{2137}{2136}$ on complete graphs. Our reduction also works for Correlation Clustering (CC), the Cluster Deletion problem (CD) and the Minimum Strong Triadic Closure problem (MinSTC). Lastly, we show that the BTT and CC optima are within a factor of 3/2 in complete graphs, by describing a pivot procedure that transforms transversals into clusters.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。