Regularization methods for prediction in dynamic graphs and e-marketing applications.

Summary The prediction of connections between objects, based either on a noisy observation or on a sequence of observations, is a problem of interest for a number of applications ranging from the design of recommendation systems in e-commerce and social networks to network inference in molecular biology. This work presents formulations of the link prediction problem, in both static and temporal settings, as a regularized problem. In the static scenario it is the combination of two well-known norms, the L1-norm and the trace-norm that allows link prediction, while in the dynamic case the use of an autoregressive model on linear descriptors allows to improve the quality of prediction. We will study the nature of the solutions of the optimization problems both in statistical and algorithmic terms. Encouraging empirical results highlight the contribution of the adopted methodology.