Towards time-aware link prediction in evolving social networks

Prediction of links- both new as well as recurring- in a social net-work representing interactions between individuals is an important problem. In the recent years, there is significant interest in meth-ods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., their evolution over time. In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that time-stamps of past interactions significantly improve the prediction ac-curacy of new and recurrent links over rather sophisticated meth-ods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches