Dynamic Mixed Membership Blockmodel for Evolving Networks
Abstract
In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor can assume multiple roles and their degrees of affiliation to these roles can also exhibit rich temporal phenomena. We propose a state space mixed membership stochastic blockmodel which can track across time the evolving roles of the actors. We also derive an efficient variational inference procedure for our model, and apply it to the Enron email networks, and rewiring gene regulatory networks of yeast. In both cases, our model reveals interesting dynamical roles of the actors.
Cite
Text
Fu et al. "Dynamic Mixed Membership Blockmodel for Evolving Networks." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553416Markdown
[Fu et al. "Dynamic Mixed Membership Blockmodel for Evolving Networks." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/fu2009icml-dynamic/) doi:10.1145/1553374.1553416BibTeX
@inproceedings{fu2009icml-dynamic,
title = {{Dynamic Mixed Membership Blockmodel for Evolving Networks}},
author = {Fu, Wenjie and Song, Le and Xing, Eric P.},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {329-336},
doi = {10.1145/1553374.1553416},
url = {https://mlanthology.org/icml/2009/fu2009icml-dynamic/}
}