A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks


James Foulds, Christopher DuBois, Arthur Asuncion, Carter Butts, Padhraic Smyth ;
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:287-295, 2011.


Real-world relational data sets, such as social networks, often involve measurements over time. We propose a Bayesian nonparametric latent feature model for such data, where the latent features for each actor in the network evolve according to a Markov process, extending recent work on similar models for static networks. We show how the number of features and their trajectories for each actor can be inferred simultaneously and demonstrate the utility of this model on prediction tasks using both synthetic and real-world data. [pdf]

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