Distributed network decomposition: A probabilistic greedy approach

Author

Zhang, Yanbing ; Dai, Huaiyu

Author_Institution

Dept. of Electr. & Comput. Eng., NC State Univ., Raleigh, NC, USA

fYear

2010

fDate

14-19 March 2010

Firstpage

3338

Lastpage

3341

Abstract

In this paper, we propose a novel distributed network decomposition algorithm with the aid of the factor graph model and the max-product algorithm, which aims to achieve minimum cut weight. Its effectiveness is testified for general graph partition as well as distributed inference in wireless networks. Our algorithm is fully distributed, simple in computation, and readily extensible, thus providing a potentially powerful, data-independent clustering scheme for a wide range of data processing and networking applications.

Keywords

graph theory; probability; radio networks; data processing; data-independent clustering scheme; distributed inference; distributed network decomposition; factor graph model; general graph partition; max-product algorithm; minimum cut weight; networking applications; probabilistic greedy approach; wireless networks; Clustering algorithms; Computer networks; Data processing; Distributed computing; Inference algorithms; Matrix decomposition; Partitioning algorithms; Symmetric matrices; Testing; Wireless networks; Network decomposition; distributed clustering; factor graph; max-product algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Conference_Location

Dallas, TX

ISSN

1520-6149

Print_ISBN

978-1-4244-4295-9

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2010.5496009

Filename

5496009