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Continuous-Time Regression Models for Longitudinal Networks
Duy Q Vu · Arthur Asuncion · David Hunter · Padhraic Smyth

Thu Dec 15 01:20 AM -- 01:40 AM (PST) @ None

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.

Author Information

Duy Q Vu (Pennsylvania State University)
Arthur Asuncion (University of California, Irvine)
David Hunter (Penn State University)
Padhraic Smyth (University of California, Irvine)

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