Modeling Group Dynamics Using Probabilistic Tensor Decompositions
This work addresses the challenge of understanding collective social dynamics, which is incremental as it builds on existing probabilistic and tensor methods.
The authors tackled the problem of modeling dynamic group behaviors in social agents by proposing a probabilistic framework that uses hierarchical Bayesian processes and hidden Markov models to capture evolving group structures, and they developed an efficient inference method based on tensor decompositions and EM for parameter estimation.
We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources. The proposed model is based on a hierarchical Bayesian process, in which each observation is a finite mixture of an set of latent groups and the mixture proportions (i.e., group probabilities) are drawn randomly. Each group is associated with some distributions over a finite set of outcomes. Moreover, as time evolves, the structure of these groups also changes; we model the change in the group structure by a hidden Markov model (HMM) with a fixed transition probability. We present an efficient inference method based on tensor decompositions and the expectation-maximization (EM) algorithm for parameter estimation.