Variable Selection for Dynamic Citation Networks

• Activity Number: 326
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Xizhen Cai* and David R. Hunter
• Companies: Carnegie Mellon University and Penn State
• Keywords: Cox's model ; penalized partial likelihood ; case-control approximation

Abstract

Recently, survival models like the Cox model are also extended to apply to dynamic network data (Vu et al., 2011b; Perry and Wolfe, 2013), where the observations are dependent. We extend the penalization idea to the Cox model in an egocentric approach to dynamic networks, and select covariates by maximizing the penalized partial likelihood function. Asymptotic properties of both the unpenalized and penalized partial likelihood estimates are developed under certain regularity conditions. We also implement the estimation and test the prediction performance of these estimates in a citation network. Since the covariates are time-varying, the computation cost is high. After variable selection, the model is reduced, which simplifies the calculation for future predictions. Another method to reduce the computational complexity is to use the case-control approximation, in which instead of using all the at-risk nodes in the network, only a subset is sampled to evaluate the partial likelihood function. By using this approximation, the computation time is shortened dramatically, while the prediction performance is still satisfactory in the citation network.