Modeling Group Dynamics Using Probabilistic Tensor Decompositions

In this paper, we consider the problem of modeling discrete social network data and learning the underlying group dynamics. The goal is to develop probabilistic profiles of large collections of data while preserving the essential temporal relationships that provide insights for various applications of interest. For example, in social network analysis, we want to analyze relationships between social agents and their behaviors over time and on various social media sites (i.e., Facebook, Twitter, Instagram, Google, etc.). In web advertising analysis, we want to analyze the relationships between customers and the types of products they buy from different shopping sites to capture customers' buying behaviors and learn the intrinsic factors that effect their buying decision process. In the study of scientific collaboration, using co-authorship networks from multiple journals on related subjects, one can analyze relationships between subjects and authors.