Patterns of Influence in a Recommendation Network

  • Jure Leskovec
  • Ajit Singh
  • Jon Kleinberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. We consider information cascades in the context of recommendations, and in particular study the patterns of cascading recommendations that arise in large social networks. We investigate a large person-to-person recommendation network, consisting of four million people who made sixteen million recommendations on half a million products. Such a dataset allows us to pose a number of fundamental questions: What kinds of cascades arise frequently in real life? What features distinguish them? We enumerate and count cascade subgraphs on large directed graphs; as one component of this, we develop a novel efficient heuristic based on graph isomorphism testing that scales to large datasets. We discover novel patterns: the distribution of cascade sizes is approximately heavy-tailed; cascades tend to be shallow, but occasional large bursts of propagation can occur. The relative abundance of different cascade subgraphs suggests subtle properties of the underlying social network and recommendation process.


Product Type Graph Isomorphism Dense Subgraph Frequent Subgraph Information Cascade 
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.
    Adamic, L., Glance, N.: The political blogosphere and the 2004 US election: Divided they blog. Report (March 2005)Google Scholar
  2. 2.
    Adar, E., Adamic, L.A.: Tracking information epidemics in blogspace (2005)Google Scholar
  3. 3.
    Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashion, custom, and cultural change as informational cascades. J. of Political Economy 5 (1992)Google Scholar
  4. 4.
    Desikan, P., Srivastava, J.: Mining temporally evolving graphs. In: WebKDD (2004)Google Scholar
  5. 5.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12 (2001)Google Scholar
  6. 6.
    Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  7. 7.
    Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. SIGKDD Explorations 6(2), 43–52 (2004)CrossRefGoogle Scholar
  8. 8.
    Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)Google Scholar
  10. 10.
    Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: On the bursty evolution of blogspace. In: WWW 2003, pp. 568–576. ACM Press, New York (2003)Google Scholar
  11. 11.
    Kuramochi, M., Karypis, G.: An efficient algorithm for discovering frequent subgraphs. IEEE Trans. on Knowledge and Data Engineering 16(9) (2004)Google Scholar
  12. 12.
    Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing (2005)Google Scholar
  13. 13.
    Newman, M.: The spread of epidemic disease on networks. Phys. Rev. E 66 (2002)Google Scholar
  14. 14.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD 2002 (2002)Google Scholar
  15. 15.
    Rogers, E.: Diffusion of innovations, 4th edn. Free Press, New York (1995)Google Scholar
  16. 16.
    Watts, D.: A simple model of global cascades on random networks. In: PNAS (2002)Google Scholar
  17. 17.
    Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: ICDM 2002, pp. 721–724 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jure Leskovec
    • 1
  • Ajit Singh
    • 1
  • Jon Kleinberg
    • 2
  1. 1.School of Computer ScienceCarnegie Mellon UniversityUSA
  2. 2.Department of Computer ScienceCornell UniversityUSA

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