Using a Time based Relationship Weighting Criterion to Improve Link Prediction in Social Networks

For the last years, a considerable amount of attention has been devoted to the research about the link prediction (LP) problem in complex networks. This problem tries to predict the likelihood of an association between two not interconnected nodes in a network to appear in the future. Various methods have been developed to solve this problem. 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 traditional weighted similarity metrics in ten co-authorship networks confirm our hypothesis that weighting links based on temporal information may, in fact, improve link prediction. Proposed criterion formulation, experimental procedure and results from the performed experiment are discussed in detail.

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