An edge creation history retrieval based method to predict links in social networks

Abstract

Link prediction is a graph mining task that aims to foretell whether pairs of non-linked nodes will connect in the future. It has many useful applications in social networks such as friend recommendation, identification of future collaborations between authors in co-authorship networks, discovery of hidden groups of terrorists and criminals, among others. In general, the state-of-the-art link prediction methods consider topological data extracted from the current state (i.e., the most recent and available snapshot) of a network. They do not take into account information that describes how the network’s topology was at the moments when the existing edges were created. Hence, those methods take the chance to disregard information about the circumstances that may have influenced the appearance of old edges, and that could be useful to predict the creation of new ones. Thus, this study raises and evaluates the hypothesis that recovering such data may contribute to improving link prediction. This hypothesis is justified since those data enrich the description of the application’s context with examples that represent exactly the kind of event to be foreseen: the creation of new connections. To this end, this paper proposes a new link prediction method that is based on edge creation history retrieval. Results from experiments with twenty scenarios of four real co-authorship social networks presented statistical evidence that indicates the effectiveness of the proposed method and confirms the raised hypothesis.