The Three Dimensions of Social Prominence

  • Diego Pennacchioli
  • Giulio Rossetti
  • Luca Pappalardo
  • Dino Pedreschi
  • Fosca Giannotti
  • Michele Coscia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8238)


One classic problem definition in social network analysis is the study of diffusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of influenced nodes, but this approach misses the fact that different scenarios imply different diffusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we measure three different dimensions of social prominence: the Width, i.e. the ratio of neighbors influenced by a node; the Depth, i.e. the degrees of separation from a node to the nodes perceiving its prominence; and the Strength, i.e. the intensity of the prominence of a node. By defining a procedure to extract prominent users in complex networks, we detect associations between the three dimensions of social prominence and classical network statistics. We validate our results on a social network extracted from the Last.Fm music platform.


Social Network Analysis Closeness Centrality Social Graph Music Genre Revealed Comparative Advantage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Berlingerio, M., Coscia, M., Giannotti, F.: Mining the temporal dimension of the information propagation. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 237–248. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Burt, R.S.: Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology 92(6), 1287–1335 (1987)CrossRefGoogle Scholar
  3. 3.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: The million follower fallacy. In: ICWSM (2010)Google Scholar
  4. 4.
    Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 years. New England Journal of Medicine 357(4), 370–379 (2007)CrossRefGoogle Scholar
  5. 5.
    Christakis, N.A., Fowler, J.H.: The collective dynamics of smoking in a large social network. New England Jou. of Medicine 358(21), 2249–2258 (2008)CrossRefGoogle Scholar
  6. 6.
    Colizza, V., Barrat, A., Barthelemy, M., Valleron, A.-J., Vespignani, A.: Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLoS Medicine 4(1), e13 (2007)Google Scholar
  7. 7.
    Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(10), 1367–1372 (2004)CrossRefGoogle Scholar
  8. 8.
    Coscia, M.: Competition and success in the meme pool: a case study on In: ICWSM (2013)Google Scholar
  9. 9.
    Fowler, J.H., Christakis, N.A.: Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the framingham heart study. Bmj Clinical Research Ed. 337(2), a2338–a2338 (2008)Google Scholar
  10. 10.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Discovering leaders from community actions. In: CIKM, pp. 499–508 (2008)Google Scholar
  11. 11.
    Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. The Royal Society of London Series A 115(772), 700–721 (1927)CrossRefzbMATHGoogle Scholar
  12. 12.
    Kiang, M.Y., Kumar, A.: A comparative analysis of an extended som network and k-means analysis. Int. J. Know.-Based Intell. Eng. Syst. 8(1), 9–15 (2004)Google Scholar
  13. 13.
    Kohonen, T.: The self-organizing map. IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  14. 14.
    Kumar, U.A., Dhamija, Y.: A comparative analysis of som neural network with k-means clustering algorithm. In: Proceedings of IEEE International Conference on Management of Innovation and Technology, pp. 55–59 (2004)Google Scholar
  15. 15.
    Liu, Y.-Y., Slotine, J.-J., Barabasi, A.-L.: Controllability of complex networks. Nature 473(7346), 167–173 (2011)CrossRefGoogle Scholar
  16. 16.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Physical Review Letters 86(14), 3200–3203 (2001)CrossRefGoogle Scholar
  17. 17.
    Wang, P., González, M.C., Hidalgo, C.A., Barabási, A.-L.: Understanding the spreading patterns of mobile phone viruses. Science 324(5930), 1071–1076 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Diego Pennacchioli
    • 1
  • Giulio Rossetti
    • 1
  • Luca Pappalardo
    • 1
  • Dino Pedreschi
    • 1
  • Fosca Giannotti
    • 1
  • Michele Coscia
    • 2
  1. 1.KDDLabISTI-CNRPisaItaly
  2. 2.CID Harvard UniversityCambridgeUS

Personalised recommendations