Using a Time-Based Weighting Criterion to Enhance Link Prediction in Social Networks

Recently, the link prediction (LP) problem has attracted much attention from both scientific and industrial communities. This problem tries to predict whether two not linked nodes in a network will connect in the future. Several studies have been proposed to solve it. Some of them compute a compatibility degree (link strength) between connected nodes and apply similarity metrics between non-connected nodes in order to identify potential links. However, despite the acknowledged importance of temporal data for the LP problem, few initiatives investigated the use of this kind of information to represent link strength. In this paper, we propose a weighting criterion that combines the frequency of interactions and temporal information about them in order to define the link strength between pairs of connected nodes. The results of our experiment with weighted and non-weighted similarity metrics in ten co-authorship networks present statistical evidences that confirm our hypothesis that weighting links based on temporal information may, in fact, improve link prediction.