Link discovery in social networks

We study the problem of extracting information from social networks whose structure is initially unknown. Given a network G = (V, E) with unknown edge set E, the objective is to discover as many links as possible by applying a limited number of edge tests. Each such test reveals for a given pair of nodes v₁, v₂ ∈ V whether or not they are connected. Outcomes of previous tests can be taken into account for deciding which edge to test next. As a basic approach to this link discovery problem, we evaluate the performance of a number of scoring functions which have been proposed for predicting the future evolution of social networks. Furthermore, we develop a mechanism to apply supervised learning methods to the link discovery problem, where edge tests are utilized both for feature extraction and training sample generation. Our experiments with real and synthetic social networks demonstrate that, despite the sparsity of social network graphs, link discovery methods show a success rate improving upon random guessing by factors of up to 7. In addition, we also evaluate our link discovery methods in the context of an application where the nodes of the graph represent moving objects and only links to nearby objects are beneficial to be discovered. Also here we achieve comparable success rates.