TY - JOUR
T1 - Min-max graph partitioning and small set expansion
AU - Bansal, Nikhil
AU - Feige, Uriel
AU - Krauthgamer, Robert
AU - Makarychev, Konstantin
AU - Nagarajan, Viswanath
AU - Naor, Joseph Seffi
AU - Schwartz, Roy
PY - 2014
Y1 - 2014
N2 - 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 where the k parts need to be of equal size, and where they must separate a set of k given terminals. We consider a common generalization of these two problems, and design for it an O( v log n log k) approximation algorithm. This improves over an O(log2 n) approximation for the second version due to Svitkina and Tardos [Min-max multiway cut, in APPROX-RANDOM, 2004, Springer, Berlin, 2004], and roughly O(k log n) approximation for the first version that follows from other previous work. We also give an 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 nonempty set S ? V of size |S| ≤pn with minimum edge expansion. We give an O(log n log (1/p)) bicriteria approximation algorithm for small-set expansion in general graphs, and an improved factor of O(1) for graphs that exclude any fixed minor.
AB - 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 where the k parts need to be of equal size, and where they must separate a set of k given terminals. We consider a common generalization of these two problems, and design for it an O( v log n log k) approximation algorithm. This improves over an O(log2 n) approximation for the second version due to Svitkina and Tardos [Min-max multiway cut, in APPROX-RANDOM, 2004, Springer, Berlin, 2004], and roughly O(k log n) approximation for the first version that follows from other previous work. We also give an 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 nonempty set S ? V of size |S| ≤pn with minimum edge expansion. We give an O(log n log (1/p)) bicriteria approximation algorithm for small-set expansion in general graphs, and an improved factor of O(1) for graphs that exclude any fixed minor.
KW - Balanced cut
KW - Expansion
KW - Sparse cut
KW - Spreading metrics
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U2 - 10.1137/120873996
DO - 10.1137/120873996
M3 - Article
AN - SCOPUS:84899646612
SN - 0097-5397
VL - 43
SP - 872
EP - 904
JO - SIAM Journal on Computing
JF - SIAM Journal on Computing
IS - 2
ER -