Probabilistic framework for network partition

Tiejun Li, Jian Liu, E. W. Weinan

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Given a large and complex network, we would like to find the partition of this network into a small number of clusters. This question has been addressed in many different ways. In a previous paper, we proposed a deterministic framework for an optimal partition of a network as well as the associated algorithms. In this paper, we extend this framework to a probabilistic setting, in which each node has a certain probability of belonging to a certain cluster. Two classes of numerical algorithms for such a probabilistic network partition are presented and tested. Application to three representative examples is discussed.

Original languageEnglish (US)
Article number026106
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume80
Issue number2
DOIs
StatePublished - Aug 7 2009

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Fingerprint Dive into the research topics of 'Probabilistic framework for network partition'. Together they form a unique fingerprint.

  • Cite this