Predicting aggregate social activities using continuous-time stochastic process

Shu Huang, Min Chen, Bo Luo, Dongwon Lee

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

13 Citations (Scopus)

Abstract

How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages982-991
Number of pages10
DOIs
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

Fingerprint

Random processes
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Huang, S., Chen, M., Luo, B., & Lee, D. (2012). Predicting aggregate social activities using continuous-time stochastic process. In CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 982-991). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2396761.2396885
Huang, Shu ; Chen, Min ; Luo, Bo ; Lee, Dongwon. / Predicting aggregate social activities using continuous-time stochastic process. CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. pp. 982-991 (ACM International Conference Proceeding Series).
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Huang, S, Chen, M, Luo, B & Lee, D 2012, Predicting aggregate social activities using continuous-time stochastic process. in CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM International Conference Proceeding Series, pp. 982-991, 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 10/29/12. https://doi.org/10.1145/2396761.2396885

Predicting aggregate social activities using continuous-time stochastic process. / Huang, Shu; Chen, Min; Luo, Bo; Lee, Dongwon.

CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. p. 982-991 (ACM International Conference Proceeding Series).

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

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Huang S, Chen M, Luo B, Lee D. Predicting aggregate social activities using continuous-time stochastic process. In CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. p. 982-991. (ACM International Conference Proceeding Series). https://doi.org/10.1145/2396761.2396885