Ego-Centric Network Sampling in Viral Marketing Applications

  • Huaiyu (Harry) Ma
  • Steven Gustafson
  • Abha Moitra
  • David Bracewell
Part of the Studies in Computational Intelligence book series (SCI, volume 288)


Marketing is most successful when people spread the message within their social network. The Internet can serve as an approximation of the spread of messages, particularly marketing campaigns, to both measure marketing effectiveness and provide data for influencing future efforts. However, to measure the network of web sites spreading the marketing message potentially requires a massive amount of data collection over a long period of time. Additionally, collecting data from the Internet is very noisy and can create a false sense of precision. Therefore, we propose to use ego-centric network sampling to both reduce the amount of data required to collect as well as handle the inherent uncertainty of the data collected. In this the book chapter, we study whether the proposed ego-centric network sampling accurately captures the network structure.We use the Stanford-Berkeley network to show that the approach can capture the underlying structure with a minimal amount of data.


Degree Distribution Social Network Analysis Marketing Campaign Random Edge Burning Probability 
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.


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  1. 1.
    Bar-Yossef, Z., Mashiach, L.T.: Local approximation of pagerank and reverse pagerank. In: CIKM 2008: Proceeding of the 17th ACM conference on Information and knowledge management, pp. 279–288. ACM, New York (2008), CrossRefGoogle Scholar
  2. 2.
    Bharat, K., Chang, B.W., Henzinger, M.R., Ruhl, M.: Who links to whom: Mining linkage between web sites. In: Proceedings of the IEEE International Conference on Data Mining (2001)Google Scholar
  3. 3.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998), CrossRefGoogle Scholar
  4. 4.
    Domingos, P.: Mining social networks for viral marketing. IEEE Intelligent Systems 20(1), 80–82 (2005)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Faloutsos, J.L.C.: Sampling from large graphs. In: KDD 2006: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 631–636. ACM, New York (2006), Google Scholar
  6. 6.
    Henzinger, M.R., Heydon, A., Mitzenmacher, M., Najork, M.: On near-uniform url sampling. Comput. Netw. 33(1-6), 295–308 (2000), CrossRefGoogle Scholar
  7. 7.
    Kamvar, S., Haveliwala, T., Manning, C., Golub, G.: Exploiting the block structure of the web for computing pagerank. Tech. rep. Stanford University (2003)Google Scholar
  8. 8.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999), zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Latapy, M., Magnien, C.: Complex network measurements: Estimating the relevance of observed properties, pp. 1660–1668 (2008), doi:10.1109/INFOCOM.2008.227Google Scholar
  10. 10.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: IMC 2007: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pp. 29–42. ACM, New York (2007), CrossRefGoogle Scholar
  11. 11.
    Newman, M., Watts, D., Strogatz, S.: Random graph models of social networks. Proc. Natl. Acad. Sci. (to appear) (2002)Google Scholar
  12. 12.
    Newman, M.E.J.: Ego-centered networks and the ripple effect. Social Networks 25(1), 83–95 (2003), doi:10.1016/S0378-8733(02)00039-4CrossRefGoogle Scholar
  13. 13.
    Park, H.W.: Hyperlink network analysis: A new method for the study of social structure on the web. Connections 25(1), 49–61 (2003)Google Scholar
  14. 14.
    Reicheld, F.: The one number you need to grow. Harvard Business Review 81, 47–54 (2003)Google Scholar
  15. 15.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. ACM, New York (2002), CrossRefGoogle Scholar
  16. 16.
    Salganik, M.J., Heckathorn, D.D.: Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology 34(1), 193–240 (2004)CrossRefGoogle Scholar
  17. 17.
    Yook, S.-H., Jeong, H., Barabási, A.-L.: Modeling the internet’s large-scale topology. PNAS 99, 13,382–13,386 (2002)Google Scholar
  18. 18.
    Valente, T.W., Coronges, K., Lakon, C., Costenbader, E.: How correlated are network centrality measures? Connections 28(1), 16–26 (2008)Google Scholar
  19. 19.
    Valente, T.W., Davis, R.L.: Accelerating the diffusion of innovations using opinion leaders. The Annals of the American Academy of the Political and Social Sciences 566, 55–67 (1999)CrossRefGoogle Scholar
  20. 20.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)Google Scholar
  21. 21.
    Watts, D., Dodds, P.: Networks, influence, and public opinion formation. Journal of Consumer Research 34(4), 441–458 (2007), CrossRefGoogle Scholar
  22. 22.
    Wellman, B., Carrington, P., Hall, A.: Social Structures: A Network Analysis. In: Networks as Personal Communities, pp. 130–184. Cambridge University Press, Cambridge (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Huaiyu (Harry) Ma
    • 1
  • Steven Gustafson
    • 1
  • Abha Moitra
    • 1
  • David Bracewell
    • 1
  1. 1.GE Global Research, One Research CircleNiskayuna

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