Mean Isoperimetry with Control on Outliers: Exact and Approximation Algorithms
Morteza Alimi, Amir Daneshgar, Mohammad-Hadi Foroughmand-Araabi

TL;DR
This paper introduces exact and approximation algorithms for mean isoperimetry problems on weighted graphs, including robust variants, with efficient solutions on trees and approximation guarantees on general graphs.
Contribution
It provides the first polynomial-time algorithms for mean isoperimetry with connectivity constraints on trees and approximation algorithms for general graphs using graph sparsifiers.
Findings
Exact polynomial-time algorithms for mean isoperimetry on weighted trees.
NP-hardness of the problem on general weighted trees.
Polynomial-time approximation algorithms for general graphs using graph sparsifiers.
Abstract
Given a weighted graph with weight functions and , and a subset , the normalized cut value for is defined as the sum of the weights of edges exiting divided by the weight of vertices in . The {\it mean isoperimetry problem}, , for a weighted graph is a generalization of the classical uniform sparsest cut problem in which, given a parameter , the objective is to find disjoint nonempty subsets of minimizing the average normalized cut value of the parts. The robust version of the problem seeks an optimizer where the number of vertices that fall out of the subpartition is bounded by some given integer . Our main result states that , as well as its robust version, , subjected to the condition that each…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Machine Learning and Algorithms · Optimization and Search Problems
