title: Reverse engineering a social agent-based hidden markov model--visage.
abstract: We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.
authors.affiliation: Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180, USA. chenh3@cs.rpi.edu
authors.forename: Hung-Ching Justin
authors.initials: HC
authors.lastname: Chen
authors.forename: Mark
authors.initials: M
authors.lastname: Goldberg
authors.forename: Malik
authors.initials: M
authors.lastname: Magdon-Ismail
authors.forename: William A
authors.initials: WA
authors.lastname: Wallace
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003142:Communication; D003198:Computer Simulation; D006801:Humans; D007398:Interpersonal Relations; D007858:Learning; D008390:Markov Chains; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D013997:Time Factors