Time Series Based Link Prediction

Abstract

Link prediction is a task in Social Network Analysis that consists of predicting connections that are most likely to appear considering previous observed links in a social network. The majority of works in this area only performs the task by exploring the state of the network at a specific moment to make the prediction of new links, without considering the behavior of links as time goes by. In this light, we investigate if temporal information can bring any performance gain to the link prediction task. A traditional approach for link prediction uses a chosen topological similarity metric on non-connected pairs of nodes of the network at present time to obtain a score that is going to be used by an unsupervised or a supervised method for link prediction. Our approach initially consists of building time series for each pair of non-connected nodes by computing their similarity scores at different past times. Then, we deploy a forecasting model on these time series and use their forecasts as the final scores of the pairs. Our preliminary results using two link prediction methods (unsupervised and supervised) on co-authorship networks revealed satisfactory results when temporal information was considered.