Using R to Build and Assess Network Models in Biology

G. Hartvigsen

Mathematical Modelling of Natural Phenomena (2011)

  • Volume: 6, Issue: 6, page 61-75
  • ISSN: 0973-5348

Abstract

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In this paper we build and analyze networks using the statistical and programming environment R and the igraph package. We investigate random, small-world, and scale-free networks and test a standard problem of connectivity on a random graph. We then develop a method to study how vaccination can alter the structure of a disease transmission network. We also discuss a variety of other uses for networks in biology.

How to cite

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Hartvigsen, G.. "Using R to Build and Assess Network Models in Biology." Mathematical Modelling of Natural Phenomena 6.6 (2011): 61-75. <http://eudml.org/doc/222427>.

@article{Hartvigsen2011,
abstract = {In this paper we build and analyze networks using the statistical and programming environment R and the igraph package. We investigate random, small-world, and scale-free networks and test a standard problem of connectivity on a random graph. We then develop a method to study how vaccination can alter the structure of a disease transmission network. We also discuss a variety of other uses for networks in biology. },
author = {Hartvigsen, G.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {networks; graph theory; R; igraph; modeling disease dynamics},
language = {eng},
month = {10},
number = {6},
pages = {61-75},
publisher = {EDP Sciences},
title = {Using R to Build and Assess Network Models in Biology},
url = {http://eudml.org/doc/222427},
volume = {6},
year = {2011},
}

TY - JOUR
AU - Hartvigsen, G.
TI - Using R to Build and Assess Network Models in Biology
JO - Mathematical Modelling of Natural Phenomena
DA - 2011/10//
PB - EDP Sciences
VL - 6
IS - 6
SP - 61
EP - 75
AB - In this paper we build and analyze networks using the statistical and programming environment R and the igraph package. We investigate random, small-world, and scale-free networks and test a standard problem of connectivity on a random graph. We then develop a method to study how vaccination can alter the structure of a disease transmission network. We also discuss a variety of other uses for networks in biology.
LA - eng
KW - networks; graph theory; R; igraph; modeling disease dynamics
UR - http://eudml.org/doc/222427
ER -

References

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  7. G. Hartvigsen, J.M. Dresch, A.L. Zielinski, A.J. Macula, C.C. Leary. Network structure and vaccination strategy and effort interact to affect the dynamics of influenza epidemics. Journal of Theoretical Biology, 246, Vol. 2, (2007), 205–213.  
  8. W.O. Kermack, A.G. McKendrick. A Contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115 (1927), 700–721.  Zbl53.0517.01
  9. M.E. Newman, A.-L. Barabási, D.J. Watts. The Structure and Dynamics of Networks. Princeton University Press, 2006.  
  10. M.E. Newman. Networks: an Introduction. Oxford University Press, 2010.  Zbl1195.94003
  11. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2010), Vienna, Austria. URL .  URIhttp://www.R-project.org/
  12. R. Solomonoff, A. Rapoport. Connectivity of random nets. Bulletin of Mathematical Biophysics, 13 (1951), 107.  
  13. D.J. Watts, S.H. Strogatz. Collective dynamics of "small-world" networks. Nature, 393 (1998), 440–442.  
  14. A.F. Zuur, E.N. Ieno, E.H.W.G. Meesters. A Beginner’s Guide to R, Springer, 2009.  Zbl1170.62003

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