Scheduled seeding for latent viral marketing

Alon Sela, Dmitri Goldenberg, Erez Shmueli, Irad Ben-Gal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

One highly studied topic in the field of social networks is the search for influential nodes, that when seeded (i.e. infected intentionally), may infect a large portion of the network through a viral process. However, when it comes to the spread of new products, such viral processes are rather rare. Social influence is indeed an important factor when it comes to the act of adopting a new product. However, this influence is usually latent and does not trigger the purchase action by itself, it therefore requires an additional sales effort. We propose a model and a method that better fit the product adoption scenario. Our method allocates the seeding efforts not only to precise nodes but also at precise points in time, such that the product adoption rate increases. By conducting a set of empirical simulations, we show that under realistic assumptions, our method improves the product adoption rate by 25%-50%.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages642-643
Number of pages2
ISBN (Electronic)9781509028467
DOIs
StatePublished - 21 Nov 2016
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: 18 Aug 201621 Aug 2016

Publication series

NameProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Conference

Conference2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/TerritoryUnited States
CitySan Francisco
Period18/08/1621/08/16

Keywords

  • Information Cascades
  • Information Spread
  • Scheduled Seeding
  • Social Networks
  • Viral Marketing

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