Non-negative matrix factorisation for network reordering

Clare Lee, Desmond Higham, D. Crowther, J. Keith Vass

Research output: Contribution to journalArticlepeer-review


Non-negative matrix factorisation covers a variety of algorithms that attempt to
represent a given, large, data matrix as a sum of low rank matrices with a prescribed sign pattern. There are intuitave advantages to this approach, but also theoretical and computational challenges. In this exploratory paper we investigate the use of non-negative matrix factorisation algorithms as a means to reorder the nodes in a large network. This gives a set of alternatives to the more traditional approach of using the singular value decomposition. We describe and implement a range of recently proposed algorithms and evaluate their performance on synthetically constructed test data and on a real data set arising in cancer research.
Original languageEnglish
Pages (from-to)39-53
Number of pages16
JournalMonografias de la Real Academia de Ciencias de Zaragoza
Publication statusPublished - 2010


  • matrix factorisation
  • data matrix
  • cancer research

Cite this