Learning Continuous-Time Social Network Dynamics
This work addresses the challenge of estimating parameters in social network models for researchers in sociology and machine learning, though it is incremental as it builds on existing CTBN methods.
The paper tackled the problem of modeling social network dynamics by framing sociology models as continuous-time Bayesian networks (CTBNs) and using a sampling-based inference method, achieving better accuracy in parameter estimation than standard methods, with results validated on synthetic and real data.
We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of the model than the standard method of moments algorithmfromthe sociology literature. We extend the existing social network models to allow for indirect and asynchronous observations of the links. A Markov chain Monte Carlo sampling algorithm for this new model permits estimation and inference. We provide results on both a synthetic network (for verification) and real social network data.