Inferring agent dynamics from social communication network

We present a machine learning approach to discovering the agent dynamics or micro-laws that drives the evolution of the social groups in a community. We set up the problem by introducing a parameterized probabilistic model for the agent dynamics: the acts of an agent are determined by micro-laws with unknown parameters. Our approach is to identify the appropriate micro-laws which corresponds to identifying the appropriate parameters in the model. To solve the problem we develop heuristic expectation-maximization style algorithms for determining the micro-laws of a community based on either the observed social group evolution, or observed set of communications between actors. We present the results of extensive experiments on simulated data as well as some results on real communities, e.g., newsgroups.