Combining contextual, temporal and topological information for unsupervised link prediction in social networks

Understanding and characterizing the processes driving social interactions is one of the fundamental problems in social network analysis. In this context, link prediction aims to foretell whether two not linked nodes in a network will connect in the near future. Several studies proposed to solve link prediction compute compatibility degrees as link weights between connected nodes and, based on a weighted graph, apply weighted similarity functions to non-connected nodes in order to identify potential new links. The weighting criteria used by those studies were based exclusively on information about the existing topology (network structure). Nevertheless, such approach leads to poor incorporation of other aspects of the social networks, such as context (node and link attributes), and temporal information (chronological interaction data). Hence, in this paper, we propose three weighting criteria that combine contextual, temporal and topological information in order to improve results in link prediction. We evaluated the proposed weighting criteria with two popular weighted similarity functions (Adamic-Adar and Common Neighbors) in ten networks frequently used in experiments with link prediction. Results with the proposed criteria were statistically better than the ones obtained from the weighting criterion that is exclusively based on topological information.