Header Home Home Research News Events About CWI Library Publications Home Contact Intranet Search


full record

A stochastic uncoupling process for graphs [ 2000 ]

CWI report R 0011


Rights: IPR: CWI
DB ID: 4462
File upload: Original publishers version
Digital object:http://oai.cwi.nl/oai/asset/4462/04462D.pdf (published version)
Persistent Identifier:urn:NBN:nl:ui:18-4462 (resolver: http://persistent-identifier.org/ - leads to CWI repository; use
http://persistent-identifier.org/?identifier=urn:nbn:nl:ui:18-4462 to refer to this repository item)
Open Access:A publicly accessible file is available in the CWI repository
Type: BibTeX type: techreport (report type: cwireport)
OAI type: preprint
Authors: Dongen, S. van (1)
Title: A stochastic uncoupling process for graphs
Abstract: A discrete stochastic uncoupling process for finite spaces is introduced, called the emph{Markov Cluster Process (MCL~process). The process takes a stochastic matrix as input, and then alternates flow expansion and flow inflation, each step defining a stochastic matrix in terms of the previous one. Flow expansion corresponds with taking the~$k^{th$~power of a stochastic matrix, where~$kinN$. Flow inflation corresponds with a parametrized operator~$Gamma_r$, $rgeq 0$, which maps the set of (column) stochastic matrices onto itself. The image~$Gamma_r M$ is obtained by raising each entry in~$M$ to the~$r^{th$~power and rescaling each column to have sum~$1$ again. In practice the process converges very fast towards a limit which is idempotent under both matrix multiplication and inflation, with quadratic convergence around the limit points. The limit is in general extremely sparse and the number of components of its associated graph may be larger than the number associated with the input matrix. This uncoupling is a desired effect as it reveals structure in the input matrix. The inflation operator~$Gamma_r$ is shown to map the class of matrices which are diagonally similar to a symmetric matrix onto itself. The term emph{diagonally positive semi-definite (dpsd) is used for matrices which are diagonally similar to a positive semi-definite matrix. It is shown that for $rinN$ and for~$M$ a stochastic dpsd matrix, the image~$Gamma_r M$ is again dpsd. Determinantal inequalities satisfied by a dpsd matrix~$M$ imply a natural ordering among the diagonal elements of~$M$, generalizing a mapping of nonnegative column allowable idempotent matrices onto overlapping clusterings. The spectrum of~$Gamma_{infty M$, for dpsd $M$, is of the form~${0^{n-k, 1^k$, where~$k$ is the number of endclasses of the ordering associated with~$M$, and~$n$ is the dimension of~$M$. Reductions of dpsd matrices are given, a connection with Hilbert's distance and the contraction ratio defined for nonnegative matrices is discussed, and several conjectures are made.
Language: en
MSC classification codes: 05B20;15B48;15B51;62H30;68R10;68T10;90C35
MSC classification description: Matrices (incidence, Hadamard, etc.); Positive matrices and their generalizations; cones of matrices; Stochastic matrices; Classification and discrimination; cluster analysis; Graph theory; Pattern recognition, speech recognition; Programming involving graphs or networks
Publisher: CWI
Size: 19p.
ISSN: 1386-3681
Series: Information Systems [INS]
Acronym: INS
Number: R 0011
Year: 2000
Pages: 1 - 19
Pages number: 19p.
ORA creation date: 2008-09-04 14:
ORA modification date: 20090918134145
Mutation date: 2009-09-18
Darenet: x
Related postprint:
Other relations:


Feedback | CWI Home page