Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for a given item. In this paper we propose an adaptation of the search algorithms proposed in [14, 15, 1] to the tag recommendation problem. Our algorithm, called STRec, provides network-aware recommendations based on proximity measures computed on-the-fly in the network. STRec uses a bounded search to find good neighbors. On top of STRec, we apply a re-ranking scheme that improves the quality of the recommendations. We update the ranking according to the degree of association between the higher ranked tags and the lower ranked ones. This technique leads to better recommendations as we show in this paper and could be applicable on top of many recommender systems. The experiments we did on several datasets demonstrated the efficiency of our approach.