Uncertainty in Artificial Intelligence
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Learning Continuous-Time Social Network Dynamics
Yu Fan, Christian Shelton
Abstract:
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.
Keywords: null
Pages: 161-168
PS Link:
PDF Link: /papers/09/p161-fan.pdf
BibTex:
@INPROCEEDINGS{Fan09,
AUTHOR = "Yu Fan and Christian Shelton",
TITLE = "Learning Continuous-Time Social Network Dynamics",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "161--168"
}


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