Social tagging is a popular and convenient way to organize information. Automatic tag suggestion can ease the user’s tagging activity. In this paper, we exam both content-based and graph-based methods for tag suggestion using the BibSonomy dataset, and describe our methods for ECML/PKDD Discovery Challenge 2009 submissions. In content-based tag suggestion, we propose a fast yet accurate method named Feature-Driven Tagging. In graph-based tag suggestion, we apply DiffusionRank to solve the problem, and get a better result than current state-of-the-art methods in cross-validation.