Skip to main content

Dynamic relational topic model for social network analysis with noisy links

Publication ,  Journal Article
Wang, E; Silva, J; Willett, R; Carin, L
Published in: IEEE Workshop on Statistical Signal Processing Proceedings
September 5, 2011

A probabilistic framework is presented for joint analysis of text and links between nodes (e.g., people) in a time-evolving social network. Unlike existing approaches, the proposed model is able to handle noisy links, i.e., observed links between nodes for which there is limited or no similarity in the associated text. This decoupling between links and text is made possible by incorporating random effects in the probabilistic model, and leads to improved text modeling and link prediction performance. The model allows efficient inference using fully conjugate Gibbs sampling, obviating the need for any maximum-likelihood parameter setting. Experiments are conducted using scientific paper citation and co-authorship network datasets, with the proposed approach outperforming previous state-of-the-art results. © 2011 IEEE.

Duke Scholars

Published In

IEEE Workshop on Statistical Signal Processing Proceedings

DOI

Publication Date

September 5, 2011

Start / End Page

497 / 500
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wang, E., Silva, J., Willett, R., & Carin, L. (2011). Dynamic relational topic model for social network analysis with noisy links. IEEE Workshop on Statistical Signal Processing Proceedings, 497–500. https://doi.org/10.1109/SSP.2011.5967741
Wang, E., J. Silva, R. Willett, and L. Carin. “Dynamic relational topic model for social network analysis with noisy links.” IEEE Workshop on Statistical Signal Processing Proceedings, September 5, 2011, 497–500. https://doi.org/10.1109/SSP.2011.5967741.
Wang E, Silva J, Willett R, Carin L. Dynamic relational topic model for social network analysis with noisy links. IEEE Workshop on Statistical Signal Processing Proceedings. 2011 Sep 5;497–500.
Wang, E., et al. “Dynamic relational topic model for social network analysis with noisy links.” IEEE Workshop on Statistical Signal Processing Proceedings, Sept. 2011, pp. 497–500. Scopus, doi:10.1109/SSP.2011.5967741.
Wang E, Silva J, Willett R, Carin L. Dynamic relational topic model for social network analysis with noisy links. IEEE Workshop on Statistical Signal Processing Proceedings. 2011 Sep 5;497–500.

Published In

IEEE Workshop on Statistical Signal Processing Proceedings

DOI

Publication Date

September 5, 2011

Start / End Page

497 / 500