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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)

Abstract

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.

Keywords

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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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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