Dynamic mixed membership blockmodel for evolving networks

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Dynamic mixed membership blockmodel for evolving networks

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ACM International Conference Proceeding Series; Vol. 382 archive
Proceedings of the 26th Annual International Conference on Machine Learning table of contents

Montreal, Quebec, Canada

Pages 329-336

Year of Publication: 2009

ISBN:978-1-60558-516-1

Authors

Wenjie Fu	Carnegie Mellon University, Pittsburgh, PA
Le Song	Carnegie Mellon University, Pittsburgh, PA
Eric P. Xing	Carnegie Mellon University, Pittsburgh, PA

Sponsors

: MITACS
: NSF
Microsoft Research : Microsoft Research

Publisher

ACM New York, NY, USA

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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