DocumentCode :
2159120
Title :
Time-evolving modeling of social networks
Author :
Wang, Eric ; Silva, Jorge ; Willett, Rebecca ; Carin, Lawrence
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
fYear :
2011
fDate :
22-27 May 2011
Firstpage :
2184
Lastpage :
2187
Abstract :
A statistical framework for modeling and prediction of binary matrices is presented. The method is applied to social network analysis, specifically the database of US Supreme Court rulings. It is shown that the ruling behavior of Supreme Court judges can be accurately modeled by using a small number of latent features whose values evolve with time. The learned model facilitates the discovery of inter-relationships between judges and of the gradual evolution of their stances over time. In addition, the analysis in this paper extends previous results by considering automatic estimation of the number of latent features and other model parameters, based on a nonparametric-Bayesian approach. Inference is efficiently performed using Gibbs sampling.
Keywords :
Bayes methods; matrix algebra; social networking (online); Gibbs sampling; US Supreme Court rulings; automatic estimation; nonparametric-Bayesian approach; social networks; statistical framework; time-evolving modeling; Bayesian methods; Computational modeling; Databases; Predictive models; Probabilistic logic; Social network services; Trajectory; Bayesian methods; Machine learning; Predictive models; Social networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
ISSN :
1520-6149
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2011.5946761
Filename :
5946761
Link To Document :
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