ACM Home Page
Please provide us with feedback. Feedback
An algorithm for improving graph partitions
Full text PdfPdf (418 KB)
Source Symposium on Discrete Algorithms archive
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms table of contents
San Francisco, California
Pages 651-660  
Year of Publication: 2008
Authors
Reid Andersen  Microsoft Research
Kevin J. Lang  Yahoo! Research
Sponsors
: SIAM Activity Group on Discrete Mathematics
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
Society for Industrial and Applied Mathematics  Philadelphia, PA, USA
Bibliometrics
Downloads (6 Weeks): 27,   Downloads (12 Months): 129,   Citation Count: 0
Additional Information:

abstract   references   collaborative colleagues  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   

ABSTRACT

We present an algorithm called Improve that improves a proposed partition of a graph, taking as input a subset of vertices and returning a new subset of vertices with a smaller quotient cut score. The most powerful previously known method for improving quotient cuts, which is based on parametric flow, returns a partition whose quotient cut score is at least as small as any set contained within the proposed set. For our algorithm, we can prove a stronger guarantee: the quotient score of the set returned is nearly as small as any set in the graph with which the proposed set has a larger-than-expected intersection. The algorithm finds such a set by solving a sequence of polynomially many s -- t minimum cut problems, a sequence that cannot be cast as a single parametric flow problem. We demonstrate empirically that applying Improve to the output of various graph partitioning algorithms greatly improves the quality of cuts produced without significantly impacting the running time.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
N. Alon and V. Milman. Isoperimetric inequalities for graphs and superconcentrators. J. Combin. Theory B, 38:73--88, 1985.
2
 
3
M. Balinski. On a selection problem. Management Science, (17):230--231, 1970.
 
4
S. Bhatt and F. Leighton. A framework for solving vlsi graph layout problems. J. Computer Systems Sciences, 28:300--343, 1984.
 
5
R. B. Boppana. Eigenvalues and graph bisection: An average-case analysis (extended abstract). In FOCS, pages 280--285, 1987.
6
 
7
Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. In ICCV (1), pages 377--384, 1999.
 
8
T. Bui, S. Chaudhuri, F. T. Leighton, and M. Sipser. Graph bisection algorithms with good average case behavior. In FOCS, pages 181--192, 1984.
 
9
J. Carrasco, D. Fain, K. Lang, and L. Zhukov. Clustering of bipartite advertiser-keyword graph, 2003.
 
10
F. Chung. Spectral graph theory, volume Number 92 in CBMS Regional Conference Series in Mathematics. American Mathematical Society, 1997.
 
11
 
12
 
13
U. Feige, D. Peleg, and G. Kortsarz. The dense k-subgraph problem. Algorithmica, 29(3):410--421, 2001.
 
14
 
15
 
16
 
17
18
 
19
M. Jerrum and G. B. Sorkin. Simulated annealing for graph bisection. In IEEE Symposium on Foundations of Computer Science, pages 94--103, 1993.
 
20
21
 
22
 
23
B. Kernighan and S. Lin. An effective heuristic procedure for partitioning graphs. The Bell System Technical Journal, pages 291--308, 1970.
 
24
K. Lang and S. Rao. A flow-based method for improving the expansion or conductance of graph cuts. In IPCO, pages 325--337, 2004.
 
25
F. T. Leighton and S. Rao. An approximate max-flow min-cut theorem for uniform multicommodity flow problems with applications to approximation algorithms. In FOCS, pages 422--431, 1988.
 
26
M. Narasimhan and J. Bilmes. Local search for balanced submodular clusterings. In IJCAI, pages 981--986, 2007.
 
27
 
28
J.-C. Picard and M. Queyranne. Selected applications of minimum cuts in networks. INFORM, 20(4):394--422, 1982.
 
29
 
30
J. Rhys. A selection problem of shared fixed costs and network flows. Management Science, (17):200--207, 1970.
 
31
D. Shmoys. Cut problems and their applications to divide-and-conquer, 1996.
 
32
 
33
A. J. Soper, C. Walshaw, and M. Cross. A combined evolutionary search and multilevel approach to graph partitioning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 674--681, Las Vegas, Nevada, USA, 10-12 2000. Morgan Kaufmann.
Collaborative Colleagues:
Reid Andersen: colleagues
Kevin J. Lang: colleagues