Title: A Generative Model for Dynamic Networks with Applications,
Abstract: Networks observed in real world like social networks, collaboration networks
etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear
over time. In this paper, we propose a generative, latent space based,
statistical model for such networks (called dynamic networks). We consider the
case where the number of nodes is fixed, but the presence of edges can vary
over time. Our model allows the number of communities in the network to be
different at different time steps. We use a neural network based methodology to
perform approximate inference in the proposed model and its simplified version.
Experiments done on synthetic and real world networks for the task of community
detection and link prediction demonstrate the utility and effectiveness of our
model as compared to other similar existing approaches.