A latent space model for link prediction in statistical citation network

Abstract

Link prediction is of vital importance in network analysis. In this work, we propose a novel latent space model for link prediction in a statistical citation network. Specifically, the model can incorporate the transitivity information of both the citation network and the author-paper network. In addition, nodal features are also taken into consideration and the pseudo maximum likelihood estimation of the corresponding parameter is developed. The asymptotic consistency is established and demonstrated through extensive simulation studies. Link prediction is then performed and the performance is compared among different methods. At last, a real citation network of statistics is analyzed.