Prediction of links -both new as well as recurring -in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods 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 timestamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches.
REF incorporated temporal information available on evolving social networks for link prediction tasks and proposed a novel nodecentric approach to the evaluation of link prediction.
Towards time-aware link prediction in evolving social networks
SNA-KDD '09
Computer Science