Online Egocentric Models for Citation Networks

With the emergence of large-scale evolving (timevarying) networks, dynamic network analysis (DNA) has become a very hot research topic in recent years. Although a lot of DNA methods have been proposed by researchers from different communities, most of them can only model snapshot data recorded at a very rough temporal granularity. Recently, some models have been proposed for DNA which can be used to model large-scale citation networks at a fine temporal granularity. However, they suffer from a significant decrease of accuracy over time because the learned parameters or node features are static (fixed) during the prediction process for evolving citation networks. In this paper, we propose a novel model, called online egocentric model (OEM), to learn time-varying parameters and node features for evolving citation networks. Experimental results on real-world citation networks show that our OEM can not only prevent the prediction accuracy from decreasing over time but also uncover the evolution of topics in citation networks.