Predicting aggregate social activities using continuous-time stochastic process

  • Authors:

  • Shu Huang;Min Chen;Bo Luo;Dongwon Lee

  • Affiliations:

  • The Pennsylvania State University, University Park, PA, USA;Johns Hopkins University, Baltimore, MD, USA;The University of Kansas, Lawrence, KS, USA;The Pennsylvania State University, University Park, PA, USA

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

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.