Modeling Group Dynamics Using Probabilistic Tensor Decompositions

TL;DR

This paper introduces a probabilistic tensor decomposition framework for modeling the evolving group behaviors of social agents, capturing dynamic patterns and group profiles over time using hierarchical Bayesian models and HMMs.

Contribution

It presents a novel hierarchical Bayesian model combined with tensor decompositions and HMMs for dynamic group behavior analysis, with an efficient EM-based inference method.

Findings

Effective modeling of social group dynamics over time

Accurate identification of behavioral group profiles

Efficient inference via tensor decompositions and EM

Abstract

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.