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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)

Abstract

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.

Keywords

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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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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