Photo sharing websites such as Flickr host a massive amount of social images with user-provided tags. However, these tags are often imprecise and incomplete, which essentially limits tag-based image indexing and related applications. To tackle this issue, we propose an image retagging scheme that aims at refining the quality of the tags. The retagging process is formulated as a multiple graph-based multi-label learning problem, which simultaneously explores the visual content of the images, semantic correlation of the tags as well as the prior information provided by users. Different from classical single graph-based multi-label learning algorithms, the proposed algorithm propagates the information of each tag along an individual tag-specific similarity graph, which reflects the particular relationship among the images with respect to the specific tag and at the same time the propagations of different tags interact with each other in a collaborative way with an extra tag similarity graph. In particular, we present a robust tag-specific visual sub-vocabulary learning algorithm for the construction of those tag-specific graphs. Experimental results on two benchmark Flickr image datasets demonstrate the effectiveness of our proposed image retagging scheme. We also show the remarkable performance improvements brought by retagging in the task of image ranking.