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Dimensional Data

Bayesian Fused Lasso Regression for Dynamic Binary Networks

, &
Pages 840-850 | Received 01 Sep 2015, Published online: 13 Oct 2017
 

ABSTRACT

We propose a multinomial logistic regression model for link prediction in a time series of directed binary networks. To account for the dynamic nature of the data, we employ a dynamic model for the model parameters that is strongly connected with the fused lasso penalty. In addition to promoting sparseness, this prior allows us to explore the presence of change points in the structure of the network. We introduce fast computational algorithms for estimation and prediction using both optimization and Bayesian approaches. The performance of the model is illustrated using simulated data and data from a financial trading network in the NYMEX natural gas futures market. Supplementary material containing the trading network dataset and code to implement the algorithms is available online.

Funding

This research was partially supported by NSF/DMS award number 1441433.

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