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Just how dense are dense graphs in the real world?: a methodological note

Published:23 May 2006Publication History

ABSTRACT

This methodological note focuses on the edge density of real world examples of networks. The edge density is a parameter of interest typically when putting up user studies in an effort to prove the robustness or superiority of a novel graph visualization technique. We survey many real world examples all being of equal interest in Information Visualization, and draw a list of conclusions on how to tune edge density when randomly generating graphs in order to build artificial though realistic examples.

References

  1. M. Amiel, G. Melançon, and C. Rozenblat. Réseaux multi-niveaux: l'exemple des échanges aériens mondiaux. M@ppemonde, 78, 2005.Google ScholarGoogle Scholar
  2. D. Auber, Y. Chiricota, F. Jourdan, and G. Melançon. Multiscale navigation of small world networks. In IEEE Symposium on Information Visualisation, pages 75--81, Seattle, GA, USA, 2003. IEEE Computer Science Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A.-L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286:509--512, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  4. B. Bollobás. Random Graphs. Academic Press, London, 1985.Google ScholarGoogle Scholar
  5. S. Bornholdt and G. Schuster, editors. Handbook of Graphs and Networks: From the Genome to the Internet. Wiley-VCH, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. F. Boutin, J. Thièvre, and M. Hascoët. Focus-based filtering + clustering technique for power-law networks with small world phenomenon. In R. F. Erbacher, J. C. Roberts, M. T. Gröhn, and K. Börner, editors, SPIE Electronic Imaging/Visualization and Data Analysis, volume 6060, San Jose, California, 2006. SPIE IS&T.Google ScholarGoogle Scholar
  7. Q. Chen, H. Chang, R. Govindan, and S. Jamin. The origin of power laws in internet topologies revisited. In IEEE INFOCOM, Anchorage, Alaska, 2001. IEEE Communications Society.Google ScholarGoogle Scholar
  8. Y. Chiricota, 2006. Personal communication.Google ScholarGoogle Scholar
  9. M. Delest, T. Munzner, D. Auber, and J.-P. Domenger. Exploring infovis publication history with tulip (2nd place - infovis contest). In IEEE Symposium on Information Visualization, page 110. IEEE Computer Society, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. Dion, D. Auber, B. Leblanc, and G. Melançon. Graphe d'associations verbales: élaboration et visualisation. In Cognitique: vers une informatique plus cognitive et sociale, pages 223--232. Cépaduès-Editions, 2003.Google ScholarGoogle Scholar
  11. S. N. Dorogovtsev and J. F. F. Mendes. Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. Ebel, L.-I. Mielsch, and S. Bornholdt. Scale-free topology of e-mail networks. Physics Reviews E, 66(035103(R)), 2002.Google ScholarGoogle Scholar
  13. P. Erdos and A. Renyi. On random graphs. Publ. Math. Debrecen, 6:290--297, 1959.Google ScholarGoogle Scholar
  14. P. Erdos and A. Rényi. On the evolution of random graphs. Publ. Math. Inst. Hungar. Acad. Sci., 5:17--61, 1960.Google ScholarGoogle Scholar
  15. J.-D. Fekete, G. Grinstein, and C. Plaisant. "ieee infovis 2004 contest", the history of infovis (www.cs.umd.edu/hcil/iv04contest), 2004.Google ScholarGoogle Scholar
  16. B. Gaume, 2003. Personal communication.Google ScholarGoogle Scholar
  17. B. Gaume, N. Hathout, and P. Muller. Désambiguïsation par proximité structurelle. In Conférence Traitement Automatique du Langage Naturel (TALN'2004), pages 205--214, Fez, Maroc, 2004. ATALA.Google ScholarGoogle Scholar
  18. M. Ghoniem. Outils de visualisation et d'aide à la mise au point de programmes avec contraintes. Phd, Université de Nantes, 2005.Google ScholarGoogle Scholar
  19. M. Ghoniem, J.-D. Fekete, and P. Castagliola. Comparaison de la lisibilité des graphes en représentation noeuds-liens et matricielle. In IHM 2004, ACM International Conference Proceedings Series, pages 77--84, Namur, Belgique, 2004. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. Ghoniem, J.-D. Fekete, and P. Castagliola. A comparison of the readability of graphs using node-link and matrix-based representations, 2004.Google ScholarGoogle Scholar
  21. M. Ghoniem, J.-D. Fekete, and P. Castagliola. On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Information Visualization, 4(2):114--135, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. A. Iamnitchi, M. Ripeanu, and I. T. Foster. Small-world file-sharing communities. In IEEE INFOCOM. IEEE Communications Society, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  23. D. Jungnickel. Graphs, Networks and Algorithms. Springer Verlag, 1999.Google ScholarGoogle Scholar
  24. B. Lee, C. S. Parr, C. Plaisant, B. B. Bederson, V. D. Veklser, W. D. Gray, and C. Kotfila. Treeplus: Interactive exploration of networks with enhanced tree layouts. IEEE Transactions on Visualization and Computer Graphics, Special Issue on Visual Analytics, To appear. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. G. Melançon and I. Herman. Dag drawing from an information visualization perspective. In W. d. Leeuw and R. v. Liere, editors, Joint Eurographics and IEEE TCVG Symposium on Visualization (Data Visualization '00), pages 3--13, Amsterdam, 2000. Springer-Verlag.Google ScholarGoogle Scholar
  26. M. Newman, D. Watts, and S. Strogatz. Random graph models of social networks. Proceedings of the National Academy of Sciences, 99:2566--2572, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  27. M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167--256, 2003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. A. Noack. An energy model for visual graph clustering. In 11th International Symposium on Graph Drawing (GD 2003), volume 2912 of Lecture Notes in Computer Science, pages 425--436, Perugia, Italy, 2003.Google ScholarGoogle Scholar
  29. J. Park and M. E. J. Newman. The statistical mechanics of networks. Physics Reviews E, 70, 2004.Google ScholarGoogle Scholar
  30. A. Wagner. How the global structure of protein interaction networks evolves. Proceedings of the Royal Society London B, 270:457--466, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  31. D. Watts and S. H. Strogatz. Collective dynamics of "small-world" networks. Nature, 393:440--442, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  32. D. J. Watts. Small Worlds. Princeton University Press, 1999.Google ScholarGoogle Scholar
  33. D. J. Watts. Six Degrees: The Science of a Connected Age. W. W. Norton & Company, 2004.Google ScholarGoogle Scholar
  34. J. v. Wijk and F. v. Ham. Interactive visualization of small world graphs. In T. Munzner and M. Ward, editors, IEEE Symposium on Information Visualisation, Austin, TX, USA, 2004. IEEE Computer Science press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. S. Wuchty, A.-L. Barabasi, and M. T. Ferdig. Stable evolutionary signal in a yeast protein interaction network. BMC Evolutionary Biology, 6(8), 2006.Google ScholarGoogle Scholar

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          cover image ACM Other conferences
          BELIV '06: Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization
          May 2006
          89 pages
          ISBN:1595935622
          DOI:10.1145/1168149

          Copyright © 2006 ACM

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          Publication History

          • Published: 23 May 2006

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