Learning Continuous-Time Social Network Dynamics

Yu Fan, Christian Shelton
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:161-168, 2009.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-fan09a, title = {Learning Continuous-Time Social Network Dynamics}, author = {Fan, Yu and Shelton, Christian}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {161--168}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/fan09a/fan09a.pdf}, url = {https://proceedings.mlr.press/r7/fan09a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning Continuous-Time Social Network Dynamics %A Yu Fan %A Christian Shelton %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-fan09a %I PMLR %P 161--168 %U https://proceedings.mlr.press/r7/fan09a.html %V R7 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Fan, Y. & Shelton, C.. (2009). Learning Continuous-Time Social Network Dynamics. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:161-168 Available from https://proceedings.mlr.press/r7/fan09a.html. Reissued by PMLR on 04 October 2026.

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