Skip to main content

Approximately Factorizable Networks

  • Chapter
  • First Online:
Weighted Network Analysis
  • 3476 Accesses

Abstract

In factorizable networks, the adjacency (connection strength) between two nodes can be factored into node-specific contributions, named node “conformity”. Often the ith node conformity, CF i is approximately equal to the scaled connectivity k i  ∕ sum(k) of the ith node. We describe (a) an algorithm for computing the conformity CF and for measuring the “factorizability” of a general network, and (b) a module- and CF-based decomposition of a general adjacency matrix, which can be used to arrive at a parsimonious description of a network. Approximately factorizable networks have important practical and theoretical applications, e.g., we use them to derive relationships between network concepts. Collaborative work with Jun Dong has shown that network modules (i.e., subnetworks comprised of module nodes) tend to be approximately factorizable (Dong and Horvath BMC Syst Biol 1(1):24, 2007).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Deeds EJ, Ashenberg O, Shakhnovich EI (2006) A simple physical model for scaling in protein–protein interaction networks. Proc Natl Acad Sci USA 103(2):311–316

    Article  PubMed  CAS  Google Scholar 

  • Dong J, Horvath S (2007) Understanding network concepts in modules. BMC Syst Biol 1(1):24

    Article  PubMed  Google Scholar 

  • Gifi A (1990) Nonlinear multivariate analysis. Wiley, Chichester, England

    Google Scholar 

  • Horn RA, Johnson CR (1991) Topics in matrix analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Lange K (2004) Optimization. Springer, New York

    Google Scholar 

  • de Leeuw J, Michailidis G (2000) Block relaxation algorithms in statistics. J Comput Graph Stat 9:26–31

    Google Scholar 

  • Ranola JMO, Ahn S, Sehl ME, Smith DJ, Lange K (2010) A Poisson model for random multigraphs. Bioinformatics 26(16):2004–2011

    Article  PubMed  CAS  Google Scholar 

  • Ranola JMO, Langfelder P, Song L, Horvath S, Lange K (2011) An MM algorithm for module and propensity based decomposition of a network. UCLA Technical Report

    Google Scholar 

  • Servedio VDP, Caldarelli G, Butta P (2004) Vertex intrinsic fitness: How to produce arbitrary scale-free networks. Phys Rev E – Stat Nonlin Soft Matter Phys 70(5):056126

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Steve Horvath .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Horvath, S. (2011). Approximately Factorizable Networks. In: Weighted Network Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8819-5_2

Download citation

Publish with us

Policies and ethics