Certain relational datasets, such as those representing social networks, evolve over time, motivating the development of time-series models where the characteristics of the underlying groups of entities can adapt with time. This paper proposes the Dynamic Gamma Process Poisson Factorization for Network (D-NGPPF) modeling framework, wherein binary network entries are modeled using a truncated Poisson distribution and the ideal number of network groups is discovered from the data itself. Crucially, a Gammamarkov chain enables the characteristics of these groups to smoothly evolve over time. Exploiting the properties of the Negative Binomial distribution and a novel data augmentation technique, closed form Gibbs sampling updates are derived that yield superior empirical results for both synthetic and real world datasets.
分享
引用
