Advertisement

Comparing Overlapping Properties of Real Bipartite Networks

  • Fabien Tarissan
Part of the Emergence, Complexity and Computation book series (ECC, volume 14)

Abstract

Many real-world networks lend themselves to the use of graphs for analysing and modelling their structure. But such a simple representation has proven to miss some important and non trivial properties hidden in the bipartite structure of the networks. Recent papers have shown that overlapping properties seem to be present in bipartite networks and that it could explain better the properties observed in simple graphs. This work intends to investigate this question by studying two proposed metrics to account for overlapping structures in bipartite networks. The study, conducted on four dataset stemming from very different contexts (computer science, juridical science and social science), shows that the most popular metrics, the clustering coefficient, turns out to be less relevant that the recent redundancy coefficient to analyse intricate overlapping properties of real networks.

Keywords

Complex networks Bipartite graphs Social networks Overlapping 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ahn, Y.-Y., Ahnert, S.E., Bagrow, J.P., Barabási, A.-L.: Flavor network and the principles of food pairing. Scientific Reports 1 (2011)Google Scholar
  2. 2.
    Battiston, S., Catanzaro, M.: Statistical properties of corporate board and director networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 345–352 (2004)CrossRefGoogle Scholar
  3. 3.
    Guillaume, J.-L., Latapy, M.: Bipartite graphs as models of complex networks. Physica A: Statistical Mechanics and its Applications 371(2), 795–813 (2006)CrossRefGoogle Scholar
  4. 4.
    i Cancho, R.F., Solé, R.V.: The small world of human language. Proceedings of the Royal Society of London. Series B: Biological Sciences 268(1482), 2261–2265 (2001)CrossRefGoogle Scholar
  5. 5.
    Latapy, M., Magnien, C., Del Vecchio, N.: Basic notions for the analysis of large two-mode networks. Social Networks 30(1), 31–48 (2008)CrossRefGoogle Scholar
  6. 6.
    Le Fessant, F., Handurukande, S.B., Kermarrec, A.-M., Massoulié, L.: Clustering in peer-to-peer file sharing workloads. In: Voelker, G.M., Shenker, S. (eds.) IPTPS 2004. LNCS, vol. 3279, pp. 217–226. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Mérindol, P., Donnet, B., Bonaventure, O., Pansiot, J.-J.: On the impact of layer-2 on node degree distribution. In: Proc. ACM/USENIX Internet Measurement Conference (IMC) (November 2010)Google Scholar
  8. 8.
    Mérindol, P., Van den Schriek, V., Donnet, B., Bonaventure, O., Pansiot, J.-J.: Quantifying ASes multiconnectivity using multicast information. In: Proc. ACM/USENIX Internet Measurement Conference (IMC) (November 2009)Google Scholar
  9. 9.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and Analysis of Online Social Networks. In: Proceedings of the 5th ACM/Usenix Internet Measurement Conference (IMC 2007), San Diego, CA (October 2007)Google Scholar
  10. 10.
    Newman, M.E.J.: The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America 98(2), 404–409 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Newman, M.E., Strogatz, S.H., Watts, D.J.: Random graphs with arbitrary degree distributions. Physics Reviews E, 64 (2001)Google Scholar
  12. 12.
    Newman, M.E., Watts, D.J., Strogatz, S.H.: Random graph models of social networks. Proceedings of the National Academy of Sciences of the United States of America 99(suppl. 1), 2566–2572 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Prieur, C., Cardon, D., Beuscart, J.-S., Pissard, N., Pons, P.: The stength of weak cooperation: A case study on flickr. arXiv preprint arXiv:0802.2317 (2008)Google Scholar
  14. 14.
    Tarissan, F., Nollez-Goldbach, R.: The network of the international criminal court decisions as a complex system. In: Sanayei, A., Zelinka, I., Rossler, O.E. (eds.) ISCS 2013: Interdisciplinary Symposium on Complex Systems. Emergence, Complexity and Computation, vol. 8, pp. 225–264. Springer (2013)Google Scholar
  15. 15.
    Tarissan, F., Quoitin, B., Mérindol, P., Donnet, B., Pansiot, J.-J., Latapy, M.: Towards a bipartite graph modeling of the internet topology. Computer Networks 57(11), 2331–2347 (2013)CrossRefGoogle Scholar
  16. 16.
    Tumminello, M., Miccichè, S., Lillo, F., Piilo, J., Mantegna, R.N.: Statistically validated networks in bipartite complex systems. PloS One 6(3), e17994 (2011)Google Scholar
  17. 17.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fabien Tarissan
    • 1
  1. 1.UPMC Université Paris 6 and CNRS, UMR 7606, LIP6Sorbonne UniversitésParisFrance

Personalised recommendations