Timezone: »

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

Wed Dec 14 08:45 AM -- 02:59 PM (PST) @ None #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)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors