Temporal analysis of influence to predict users’ adoption in online social networks

Ericsson Marin; Ruocheng Guo; Paulo Shakarian

doi:10.1007/978-3-319-60240-0_31

Temporal analysis of influence to predict users’ adoption in online social networks

Ericsson Marin, Ruocheng Guo, Paulo Shakarian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English (US)
Title of host publication	Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings
Publisher	Springer Verlag
Pages	254-261
Number of pages	8
Volume	10354 LNCS
ISBN (Print)	9783319602394
DOIs	https://doi.org/10.1007/978-3-319-60240-0_31
State	Published - 2017
Event	10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017 - Washington, United States Duration: Jul 5 2017 → Jul 8 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10354 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017
Country	United States
City	Washington
Period	7/5/17 → 7/8/17

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-60240-0_31

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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

Temporal analysis of influence to predict users’ adoption in online social networks. / Marin, Ericsson; Guo, Ruocheng; Shakarian, Paulo.

Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings. Vol. 10354 LNCS Springer Verlag, 2017. p. 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10354 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10354 LNCS, Springer Verlag, pp. 254-261, 10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017, Washington, United States, 7/5/17. https://doi.org/10.1007/978-3-319-60240-0_31

Marin E, Guo R, Shakarian P. 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. Springer Verlag. 2017. p. 254-261. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-60240-0_31

Marin, Ericsson ; Guo, Ruocheng ; Shakarian, Paulo. / Temporal analysis of influence to predict users’ adoption in online social networks. Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings. Vol. 10354 LNCS Springer Verlag, 2017. pp. 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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