3 Scopus citations

Abstract

Different measures have been proposed to predict whether individuals will adopt a new behavior in online social networks, given the influence produced by their neighbors. In this paper, we show one can achieve significant improvement over these standard measures, extending them to consider a pair of time constraints. These constraints provide a better proxy for social influence, showing a stronger correlation to the probability of influence as well as the ability to predict influence.

Original languageEnglish (US)
Title of host publicationSocial, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings
PublisherSpringer Verlag
Pages254-261
Number of pages8
Volume10354 LNCS
ISBN (Print)9783319602394
DOIs
StatePublished - 2017
Event10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017 - Washington, United States
Duration: Jul 5 2017Jul 8 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10354 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017
CountryUnited States
CityWashington
Period7/5/177/8/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Marin, E., Guo, R., & Shakarian, P. (2017). Temporal analysis of influence to predict users’ adoption in online social networks. In Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings (Vol. 10354 LNCS, pp. 254-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10354 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-60240-0_31