Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes


  • Huawei Shen Chinese Academy of Sciences
  • Dashun Wang IBM Thomas J. Watson Research Center
  • Chaoming Song University of Miami
  • Albert-László Barabási Northeastern University



Social Dynamics, Popularity Prediction, Reinforced Poisson Process


An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in an array of areas. Here we propose a generative probabilistic framework using a reinforced Poisson process to explicitly model the process through which individual items gain their popularity. This model distinguishes itself from existing models via its capability of modeling the arrival process of popularity and its remarkable power at predicting the popularity of individual items. It possesses the flexibility of applying Bayesian treatment to further improve the predictive power using a conjugate prior. Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods.




How to Cite

Shen, H., Wang, D., Song, C., & Barabási, A.-L. (2014). Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).