Min-max Graph Partitioning and Small Set Expansion

N. Bansal, U. Feige, Robert Krauthgamer, K. Makarychev, V. Nagarajan, J. Naor, Roy Schwartz
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引用次数: 85

Abstract

We study graph partitioning problems from a min-max perspective, in which an input graph on n vertices should be partitioned into k parts, and the objective is to minimize the maximum number of edges leaving a single part. The two main versions we consider are: (i) the k parts need to be of equal size, and (ii) the parts must separate a set of k given terminals. We consider a common generalization of these two problems, and design for it an O(√log n log k)-approximation algorithm. This improves over an O(log2 n) approximation for the second version due to Svitkina and Tardos, and roughly O(k log n) approximation for the first version that follows from other previous work. We also give an improved O(1)-approximation algorithm for graphs that exclude any fixed minor. Our algorithm uses a new procedure for solving the Small Set Expansion problem. In this problem, we are given a graph G and the goal is to find a non-empty subset S of V of size at most pn with minimum edge-expansion. We give an O(√log n log (1/p)) bicriteria approximation algorithm for the general case of Small Set Expansion and O(1) approximation algorithm for graphs that exclude any fixed minor.
最小-最大图划分和小集展开
我们从最小-最大的角度研究图划分问题,其中n个顶点的输入图应该划分为k个部分,目标是最小化留下单个部分的最大边数。我们考虑的两个主要版本是:(i) k个部件需要大小相等,(ii)这些部件必须分开k个给定端子的集合。我们考虑了这两个问题的一般推广,并设计了一个O(√log n log k)近似算法。这改进了由于Svitkina和Tardos的第二个版本的O(log2 n)近似值,以及根据其他先前工作得出的第一个版本的大约O(k log n)近似值。我们还给出了一种改进的O(1)-逼近算法,用于排除任何固定小项的图。我们的算法采用了一种新的方法来解决小集展开问题。在这个问题中,我们给定一个图G,目标是找到一个V的非空子集S,其大小最大为pn,且边展开最小。我们给出了一般情况下小集展开的O(√log n log (1/p))双准则逼近算法和排除任何固定次元的图的O(1)逼近算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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