Continuous-Time Regression Models for Longitudinal Networks

author: Arthur Asuncion, Center for Machine Learning and Intelligent Systems, University of California, Irvine
published: Jan. 25, 2012,   recorded: December 2011,   views: 157
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Description

The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science. Leveraging ideas from survival and event history analysis, we introduce a continuous-time regression modeling framework for network event data that can incorporate both time-dependent network statistics and time-varying regression coefficients. We also develop an efficient inference scheme that allows our approach to scale to large networks. On synthetic and real-world data, empirical results demonstrate that the proposed inference approach can accurately estimate the coefficients of the regression model, which is useful for interpreting the evolution of the network; furthermore, the learned model has systematically better predictive performance compared to standard baseline methods.

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