Comparing and Visualizing the Social Spreading of Products on a Large Social Network

  • Pål Roe SundsøyEmail author
  • Johannes Bjelland
  • Geoffrey Canright
  • Kenth Engø-Monsen
  • Rich Ling
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)


By combining mobile traffic data and product adoption history from one of the markets of the telecom provider Telenor, we define and measure an adoption network—roughly, the social network among adopters. We study and compare the evolution of this adoption network over time for several products—the iPhone handset, the Doro handset, the iPad 3G and videotelephony. We show how the structure of the adoption network changes over time, and how it can be used to study the social effects of product diffusion. Specifically, we show that the evolution of the Largest Connected Component (LCC) and the size distribution of the other components vary strongly with different products. We also introduce simple tests for quantifying the social spreading effect by comparing actual product diffusion on the network to random based spreading models. As videotelephony is adopted pairwise, we suggest two types of tests: transactional- and node based adoption test. These tests indicate strong social network dependencies in adoption for all products except the Doro handset. People who talk together, are also likely to adopt together. Supporting this, we also find that adoption probability increases with the number of adopting friends for all the products in this study. We believe that the strongest spreading of adoption takes place in the dense core of the underlying network, and gives rise to a dominant LCC in the adoption network, which we call “the social network monster”. This is supported by measuring the eigenvector centrality of the adopters. We believe that the size of the monster is a good indicator for whether or not a product is going to “take off”.


Social Network Random Model Eigenvector Centrality Large Connected Component Kappa Test 
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.



All of our visualizations were produced using the open-source visualization platform from We would also like to thank Dr. Ellen Altenborg at Telenor ASA, and professor Øystein D. Fjeldstad of the Norwegian School of Management, for fruitful discussions and inspiration related to network economy and network analysis.


  1. 1.
    Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Aral, S., Muchnik, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. 106(51), 21544–21549 (2009)CrossRefGoogle Scholar
  3. 3.
    Bearman, P., Moody, J., Stovel, K.: Chains of affection: the structure of adolescent romantic and sexual networks. Am. J. Sociol. 110(1), 44–91 (2004)CrossRefGoogle Scholar
  4. 4.
    Birke, D., Swann, G.M.P.: Network effects and the choice of mobile phone operator. J Evol Econ 16, 65–84 (2006)CrossRefGoogle Scholar
  5. 5.
    Bollobas, B.: Random Graphs. Cambridge University Press, Cambridge/New York (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Bollobas, B., Riordan, O.: Mathematical Results on Scale-Free Random Graphs, pp. 1–37. Wiley, Weinheim (2002)Google Scholar
  7. 7.
    Canright, G.S., Engø-Monsen, K.: Spreading on networks: a topographic view. Complexus 3, 131–146 (2006). doi:10.1159/000094195CrossRefGoogle Scholar
  8. 8.
    Choi, H., Kim, S.-H., Lee, J.: Role of network structure and network effects in diffusion of innovations. Ind. Mark. Manag. 39, 170–177 (2010)CrossRefGoogle Scholar
  9. 9.
    Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A., Joshi, A.: Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, EDBT ’08, Nantes, vol. 261, pp. 668–677. ACM, New York (2008)Google Scholar
  10. 10.
    Dorogovtsev, S., Mendes, J.: Evolution of networks: from biological nets to the internet and WWW. Oxford University Press, Oxford (2000)Google Scholar
  11. 11.
    Dorogovtsev, S., Mendes, J.: Evolution of networks. Adv. Phys. 51, 1079–1187 (2002)CrossRefGoogle Scholar
  12. 12.
    Fetterly, D., Manasse, M., Najork, M., Wiener, J.: A large-scale study of the evolution of web pages. Softw. Pract. Exp. 34(2), 213–237 (2004)CrossRefGoogle Scholar
  13. 13.
    Eagle, N., Pentland, A., Lazer, D., Alex, P.: Inferring friendshop network structure by using mobile phone data. Natl. Acad. Sci. 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  14. 14.
    Erdös, P., Rényi, A.: On random graphs I. Publ. Math. Debr. 6, 290–297 (1959)zbMATHGoogle Scholar
  15. 15.
    Hill, S., Provost, F., Volinsky, C.: Network-based marketing: identifying likely adopters via consumer networks. Stat. Sci. 21(2), 256–276 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
  17. 17.
  18. 18.
    Kleinberg, J.: Complex networks and decentralized search algorithms. In: International Congress of Mathematicians. European Mathematical Society, Zürich (2006)Google Scholar
  19. 19.
    Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: Structure and evolution of blogspace. Commun. ACM 47(12), 35–39 (2004)CrossRefGoogle Scholar
  20. 20.
    Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: On the bursty evolution of blogspace. Worldw. Web J. 8(2), 159–178 (2005)CrossRefGoogle Scholar
  21. 21.
    Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: KDD, Philadelphia. ACM, New York (2006)Google Scholar
  22. 22.
    Leskovec, J., Faloutsos, J.K.C.: Graphs over time: densification laws, shrinking diameters, and possible explanations. In: 11th KDD, Chicago, pp. 177–187. ACM, New York (2005)Google Scholar
  23. 23.
    Nanavati, A.A., Gurumurthy, S., Das, G., Chakraborty, D., Dasgupta, K., Mukherjea, S., Joshi, A.: On the structural properties of massive telecom call graphs: findings and implications. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, CIKM ’06, Arlington, pp. 435–444. ACM, New York (2006)Google Scholar
  24. 24.
    Newman, M.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256, 2003MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Ntoulas, A., Cho, J., Olston, C.: What’s new on the web? The evolution of the web from a search engine perspective. In: 13th WWW, pp. 1–12. ACM, New York (2004)Google Scholar
  26. 26.
    Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., Barabási, A.-L.: Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. U.S.A. 104(18), 7332–7336, 2007CrossRefGoogle Scholar
  27. 27.
    Strogatz, S.: Exploring complex networks. Nature 410, 268–276 (2001)CrossRefGoogle Scholar
  28. 28.
    Sundsøy, P., Bjelland, J., Canright, G., Engø-Monsen, K., Ling, R.: Product adoption networks and their growth in a large mobile phone network. IEEE Advanced in Social Network Analysis and Mining (ASONAM 2010), Odense. IEEE, Los Alamitos (2010)Google Scholar
  29. 29.
    Van den Bulte, C., Wuyts, S.: Social networks and marketing. Marketing Science Institue, Cambridge (2007)Google Scholar
  30. 30.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge/New York (1994)CrossRefGoogle Scholar
  31. 31.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Pål Roe Sundsøy
    • 1
    Email author
  • Johannes Bjelland
    • 1
  • Geoffrey Canright
    • 1
  • Kenth Engø-Monsen
    • 1
  • Rich Ling
    • 2
  1. 1.Corporate DevelopmentTelenor ASAOsloNorway
  2. 2.IT-UniversityCopenhagenDenmark

Personalised recommendations