Supervised rank aggregation approach for link prediction in complex networks

In this paper we propose a new topological approach for link prediction in dynamic complex networks. The proposed approach applies a supervised rank aggregation method. This functions as follows: first we rank the list of unlinked nodes in a network at instant t according to different topological measures (nodes characteristics aggregation, nodes neighborhood based measures, distance based measures, etc). Each measure provides its own rank. Observing the network at instant t+1 where some new links appear, we weight each topological measure according to its performances in predicting these observed new links. These learned weights are then used in a modified version of classical computational social choice algorithms (such as Borda, Kemeny, etc) in order to have a model for predicting new links. We show the effectiveness of this approach through different experimentations applied to co-authorship networks extracted from the DBLP bibliographical database. Results we obtain, are also compared with the outcome of classical supervised machine learning based link prediction approaches applied to the same datasets.