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While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In this paper, we analyze influence factors for link prediction in human contact networks. Specifically, we consider the prediction of new links, and extend it to the analysis of recurring links. Furthermore, we consider the impact of stronger ties for the prediction. The results and insights of the analysis are a first step onto predictability applications for human contact networks.