Skip to main content
SpringerLink
Log in

NESSIE: Network Example Source Supporting Innovative Experimentation

  • Chapter
Network Science

  • 2537 Accesses

Abstract

We describe a new web-based facility that makes available some realistic examples of complex networks. NESSIE (Network Example Source Supporting Innovative Experimentation) currently contains 12 specific networks from a diverse range of application areas, with a Scottish emphasis. This collection of data sets is designed to be useful for researchers in network science who wish to evaluate new algorithms, concepts and models. The data sets are available to download in two formats (MATLAB’s .mat format and .txt files readable by packages such as Pajek), and some basic MATLAB tools for computing summary statistics are also provided.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

  1. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  2. Boisvert, R., Pozo, R., Remington, K., Barrett, R., Dongarra, J.: Matrix market: a web resource for test matrix collections. In: Boisvert, R. (ed.) The Quality of Numerical Software: Assessment and Enhancement, pp. 125–137. Chapman and Hall, London (1997)

    Google Scholar 

  3. Brunet, J.P., Tamayo, P., Golub, T.R., Mesirov, J.P.: Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA 101, 4164–4169 (2004)

    Article  Google Scholar 

  4. Croft, D.P., Krause, J., James, R.: Social networks in the guppy (Poecilia reticulata). Proc. R. Soc. Lond. B, Biol. Sci. 271, 516–519 (2004)

    Article  Google Scholar 

  5. Davis, T.: The University of Florida sparse matrix collection. Technical Report, University of Florida, USA (2007)

    Google Scholar 

  6. Erdös, P., Rényi, A.: On random graphs. Publ. Math. (Debr.) 6, 290–297 (1959)

    MATH  Google Scholar 

  7. Estrada, E.: Topological structural classes of complex networks. Phys. Rev. E 75, 016103 (2007)

    Article  Google Scholar 

  8. Fingleton, B., Fischer, M.: Neoclassical theory versus new economic geography: competing explanations of cross-regional variation in economic development. Ann. Reg. Sci. 44, 467–491 (2010)

    Article  Google Scholar 

  9. Gilbert, E.N.: Random graphs. Ann. Math. Stat. 30, 1141–1144 (1959)

    Article  MATH  Google Scholar 

  10. Gilbert, J.R., Moler, C., Schreiber, R.: Sparse matrices in MATLAB: design and implementation. SIAM J. Matrix Anal. Appl. 13, 333–356 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  11. Grindrod, P.: Range-dependent random graphs and their application to modeling large small-world proteome datasets. Phys. Rev. E 66, 066702 (2002)

    Article  Google Scholar 

  12. Higham, D.J., Kalna, G., Kibble, M.: Spectral clustering and its use in bioinformatics. J. Computational and Applied Math. 204, 25–37 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  13. Higham, D.J., Pržulj, N., Rašajski, M.: Fitting a geometric graph to a protein–protein interaction network. Bioinformatics 24, 1093–1099 (2008)

    Article  Google Scholar 

  14. Higham, D.J.: Unravelling small world networks. J. Comput. Appl. Math. 158, 61–74 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  15. Higham, D.J.: Spectral reordering of a range-dependent weighted random graph. IMA J. Numer. Anal. 25, 443–457 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Higham, D.J., Higham, N.J.: MATLAB Guide. SIAM, Philadelphia (2000), 283 pp.

    MATH  Google Scholar 

  17. Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., Sakaki, Y.: A comprehensive two-hybrid analysis to explore the yeast protein interaction interactome. Proc. Natl. Acad. Sci. USA 98(8), 4569–4574 (2001)

    Article  Google Scholar 

  18. Kleinberg, J.M.: Navigation in a small world. Nature 406, 845 (2000)

    Article  Google Scholar 

  19. Link, J.: Does food web theory work for marine ecosystems? Mar. Ecol. Prog. Ser. 230, 1–9 (2002)

    Article  Google Scholar 

  20. Milenkovic, T., Lai, J., Przulj, N.: GraphCrunch: A tool for large network analyses. BMC Bioinform. 9, 70 (2008)

    Article  Google Scholar 

  21. Morrison, J.L., Breitling, R., Higham, D.J., Gilbert, D.R.: A lock-and-key model for protein–protein interactions. Bioinformatics 2, 2012–2019 (2006)

    Article  Google Scholar 

  22. Newman, M.E.J., Moore, C., Watts, D.J.: Mean-field solution of the small-world network model. Phys. Rev. Lett. 84, 3201–3204 (2000)

    Article  Google Scholar 

  23. Penrose, M.: Geometric Random Graphs. Oxford University Press, Oxford (2003)

    Book  MATH  Google Scholar 

  24. Pržulj, N., Higham, D.J.: Modelling protein–protein interaction networks via a stickiness index. J. R. Soc. Interface 3, 711–716 (2006)

    Article  Google Scholar 

  25. Pržulj, N., Corneil, D.G., Jurisica, I.: Modeling interactome: Scale-free or geometric? Bioinformatics 20(18), 3508–3515 (2004)

    Article  Google Scholar 

  26. Rogers, S., Sceltema, R.E., Girolami, M., Breitling, R.: Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinformatics 25(4), 512–518 (2009)

    Article  Google Scholar 

  27. Taylor, A., Higham, D.J.: CONTEST: A controllable test matrix toolbox for MATLAB. ACM Trans. Math. Softw. 35, 1–17 (2009)

    Article  Google Scholar 

  28. Thomas, A., Cannings, R., Monk, N.A.M., Cannings, C.: On the structure of protein–protein interaction networks. Biochem. Soc. Trans. 31, 1491–1496 (2003)

    Article  Google Scholar 

  29. Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., Lockshon, E., Narayan, V., Srinivasan, M., Pochart, P., Qureshi-Emili, A., Li, Y., Godwin, B., Conover, D., Kalbfleish, T., Vijayadamodar, G., Yang, M., Johnston, M., Fields, S., Rothberg, J.M.: A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000)

    Article  Google Scholar 

  30. Vass, J.K., Higham, D.J., Mao, X., Crowther, D.: New controls of TCA-cycle genes revealed in networks built by discretization or correlation. Technical Report No. 10, Department of Mathematics (2009)

    Google Scholar 

  31. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)

    Article  Google Scholar 

  32. Wishart, D.: Whisky Classified: Choosing Single Malts by Flavour, 2nd edn. Pavilion Books, London (2006)

    Google Scholar 

  33. Yodzis, P.: Diffuse effects in food webs. Ecology 81, 261–266 (2000)

    Article  Google Scholar 

Download references

Acknowledgements

These networks are available from the NESSIE website, www.mathstat.strath.ac.uk/nessie. This work was supported by grants from the Medical Research Council (project grant G0601353), and the Engineering and Physical Sciences Research Council (project grant GR/S62383/01 and “Bridging the Gap”). The authors would like to thank the following colleagues for their contributions to NESSIE: Darren Croft, Ernesto Estrada, Bernard Fingleton, Nataša Pržulj, Simon Rogers and Marcus Wheel.

Author information

Authors and Affiliations

    Authors
    1. Alan Taylor
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Desmond J. Higham
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Alan Taylor .

    Editor information

    Editors and Affiliations

    1. , Dept. Mathematics, University of Strathclyde, Glasgow, G1 1XH, United Kingdom

      Ernesto Estrada

    2. , Dept. Computer & Information Sciences, University of Strathclyde, Richmond Street 26, Glasgow, G1 1XH, United Kingdom

      Maria Fox

    3. Dept. Mathematics, University of Strathclyde, Glasgow, G1 1XH, United Kingdom

      Desmond J. Higham

    4. , Dept. Physics & Applied Physics, University of Strathclyde, Rottenrow 107, Glasgow, G4 0NG, United Kingdom

      Gian-Luca Oppo

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2010 Springer-Verlag London Limited

    About this chapter

    Cite this chapter

    Taylor, A., Higham, D.J. (2010). NESSIE: Network Example Source Supporting Innovative Experimentation. In: Estrada, E., Fox, M., Higham, D., Oppo, GL. (eds) Network Science. Springer, London. https://doi.org/10.1007/978-1-84996-396-1_5

    Download citation

    • DOI: https://doi.org/10.1007/978-1-84996-396-1_5

    • Publisher Name: Springer, London

    • Print ISBN: 978-1-84996-395-4

    • Online ISBN: 978-1-84996-396-1

    • eBook Packages: Computer ScienceComputer Science (R0)

    Publish with us

    Policies and ethics

    Search

    Navigation