Evaluation of the model for individual behavior rate estimate: Social network data

Alena V. Suvorova, Alexander L. Tulupyev

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

5 Scopus citations

Abstract

The paper described the Bayesian belief network model for individual behavior rate estimate based on data about the last episodes of that behavior. For model evaluation we used data from social network VKontakte about episodes of publishing posts. The sample size is 1123 users with 160555 posts in total for the half-year period. Rate values were discretized and form eight classes. There was no statistical difference between estimated rate distribution in the group and the real rate distribution; individual rate estimates showed 91% classification accuracy. Hence, the model allows behavior rate estimating on the base of data about limited number of episodes that is often an important issue, for example, in epidemiological studies.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016
EditorsS. Shaposhnikov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages18-20
Number of pages3
ISBN (Electronic)9781467389198
DOIs
StatePublished - 22 Jul 2016
Event19th International Conference on Soft Computing and Measurements, SCM 2016 - Saint Petersburg, Russian Federation
Duration: 25 May 201627 May 2016

Conference

Conference19th International Conference on Soft Computing and Measurements, SCM 2016
CountryRussian Federation
CitySaint Petersburg
Period25/05/1627/05/16

Scopus subject areas

  • Artificial Intelligence
  • Statistics, Probability and Uncertainty
  • Control and Optimization
  • Modelling and Simulation

Keywords

  • Bayesian belief network
  • behavior episodes
  • behavior modelling
  • behavior rate
  • model evaluation

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