Bayesian Fused Lasso Regression for Dynamic Binary Networks
提出一种多项逻辑回归模型，用于预测有向二元网络时间序列中的链接，通过动态模型和融合套索惩罚来识别网络结构变化点，并给出快速估计算法。
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.