Link structure in online networks carry varying semantics. For example, Facebook links carry social semantics while LinkedIn links carry professional semantics. It has been shown that online networks are useful for predicting users’ future activities. In this paper, we introduce a new related problem: given a collection of networks, how can we learn the relative importance of each network for predicting user activities? We propose a framework that allows us to quantify the relative predictive value of each network in a setting where multiple networks are available. We give an epsilon-net algorithm to solve the problem and prove that it ﬁnds a solution that is arbitrarily close to the optimal solution. Experimentally, we focus our study on the prediction of ad clicks, where it is already known that a single social network improves prediction. The networks we study are implicit aﬃliations networks, which are based on users’ browsing history rather than declared relationships to other users. We create two networks based on covisitation to pages in the Facebook domain and Wikipedia domain. The learned relative weighting of these networks demonstrates covisitation networks are indeed useful for prediction, but that no single network is predictive of all kinds of ads. Rather, each category of ads calls for a diﬀerent weighting of these networks.