A scalable framework for modeling competitive diffusion in social networks

Matthias Broecheler, Paulo Shakarian, V. S. Subrahmanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

Abstract

Multiple phenomena often diffuse through a social network, sometimes in competition with one another. Product adoption and political elections are two examples where network diffusion is inherently competitive in nature. For example, individuals may choose to only select one product from a set of competing products (i.e. most people will need only one cell-phone provider) or can only vote for one person in a slate of political candidate (in most electoral systems). We introduce the weighted generalized annotated program (wGAP) framework for expressing competitive diffusion models. Applications are interested in the eventual results from multiple competing diffusion models (e.g. what is the likely number of sales of a given product, or how many people will support a particular candidate). We define the "most probable interpretation" (MPI) problem which technically formalizes this need. We develop algorithms to efficiently solve MPI and show experimentally that our algorithms work on graphs with millions of vertices.

Original languageEnglish (US)
Title of host publicationProceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust
Pages295-302
Number of pages8
DOIs
StatePublished - 2010
Externally publishedYes
Event2nd IEEE International Conference on Social Computing, SocialCom 2010, 2nd IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2010 - Minneapolis, MN, United States
Duration: Aug 20 2010Aug 22 2010

Other

Other2nd IEEE International Conference on Social Computing, SocialCom 2010, 2nd IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2010
CountryUnited States
CityMinneapolis, MN
Period8/20/108/22/10

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Broecheler, M., Shakarian, P., & Subrahmanian, V. S. (2010). A scalable framework for modeling competitive diffusion in social networks. In Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust (pp. 295-302). [5591228] https://doi.org/10.1109/SocialCom.2010.49

A scalable framework for modeling competitive diffusion in social networks. / Broecheler, Matthias; Shakarian, Paulo; Subrahmanian, V. S.

Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust. 2010. p. 295-302 5591228.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Broecheler, M, Shakarian, P & Subrahmanian, VS 2010, A scalable framework for modeling competitive diffusion in social networks. in Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust., 5591228, pp. 295-302, 2nd IEEE International Conference on Social Computing, SocialCom 2010, 2nd IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2010, Minneapolis, MN, United States, 8/20/10. https://doi.org/10.1109/SocialCom.2010.49
Broecheler M, Shakarian P, Subrahmanian VS. A scalable framework for modeling competitive diffusion in social networks. In Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust. 2010. p. 295-302. 5591228 https://doi.org/10.1109/SocialCom.2010.49
Broecheler, Matthias ; Shakarian, Paulo ; Subrahmanian, V. S. / A scalable framework for modeling competitive diffusion in social networks. Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust. 2010. pp. 295-302
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