  Online systems where users purchase or collect items of some kind can be
effectively represented by temporal bipartite networks where both nodes and
links are added with time. We use this representation to predict which items
might become popular in the near future. Various prediction methods are
evaluated on three distinct datasets originating from popular online services
(Movielens, Netflix, and Digg). We show that the prediction performance can be
further enhanced if the user social network is known and centrality of
individual users in this network is used to weight their actions.
