Khandekar, Rohit ;
Hildrum, Kirsten ;
Parekh, Sujay ;
Rajan, Deepak ;
Sethuraman, Jay ;
Wolf, Joel
Bounded Size Graph Clustering with Applications to Stream Processing
Abstract
We introduce a graph clustering problem motivated by a stream processing application. Input to our problem is an undirected graph with vertex and edge weights. A cluster is a subset of the vertices. The {\em size} of a cluster is
defined as the total vertex weight in the subset plus the total edge weight at the boundary of the cluster. The bounded size graph clustering problem ($\GC$) is to partition the vertices into clusters of size at most a given budget and minimize the total edgeweight across the clusters. In the {\em multiway cut} version of the problem, we are also given a subset of vertices called {\em terminals}. No cluster is allowed to contain more than one terminal. Our problem differs from most of the previously studied clustering problems in that the number of clusters is not specified. We first show that the feasibility version of the multiway cut $\GC$ problem,
i.e., determining if there exists a clustering with boundedsize clusters satisfying the multiway cut constraint, can be solved in polynomial time. Our algorithm is based on the mincut subroutine and an uncrossing argument. This result is in contrast with the NPhardness of the minmax multiway cut problem, considered by Svitkina and Tardos (2004), in which the number of clusters must equal the number of terminals. Our results for the feasibility version also generalize to any symmetric submodular function. We next show that the optimization version of $\GC$ is NPhard by showing an
approximationpreserving reduction from the $\frac 13$balanced cut problem.
Our main result is an $O(\log^2 n)$approximation to the optimization version
of the multiway cut $\GC$ problem violating the budget by an $O(\log n)$
factor, where $n$ denotes the number of vertices. Our algorithm is based on a
setcoverlike greedy approach which iteratively computes boundedsize clusters
to maximize the number of new vertices covered.
BibTeX  Entry
@InProceedings{khandekar_et_al:LIPIcs:2009:2325,
author = {Rohit Khandekar and Kirsten Hildrum and Sujay Parekh and Deepak Rajan and Jay Sethuraman and Joel Wolf},
title = {{Bounded Size Graph Clustering with Applications to Stream Processing}},
booktitle = {IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science},
pages = {275286},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783939897132},
ISSN = {18688969},
year = {2009},
volume = {4},
editor = {Ravi Kannan and K. Narayan Kumar},
publisher = {Schloss DagstuhlLeibnizZentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2009/2325},
URN = {urn:nbn:de:0030drops23250},
doi = {http://dx.doi.org/10.4230/LIPIcs.FSTTCS.2009.2325},
annote = {Keywords: Graph partitioning, uncrossing, GomoryHu trees, symmetric submodular functions}
}
Keywords: 

Graph partitioning, uncrossing, GomoryHu trees, symmetric submodular functions 
Seminar: 

IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science

Issue date: 

2009 
Date of publication: 

2009 