This paper proposes a new approach that uses social networks and common sense deduction rules to adapt the description tags of the photos for the current viewer. We exploit social graphs to enrich the tags associated to the concerned persons in the photo by following the different links between people (i.e. viewer and captured people in the photos). The main contributions of our work are: (i) addition of a more meaningful tagging layer for photos, making tags dynamic and auto-adaptable thanks to the automatic identification of the social context of the visualization. (ii) Due to this dynamics, the search in the social graphs is optimized using a data mining technique. (iii) we propose a new visualization metaphor for the tagging layer to manage users' feedback. We also describe a system architecture and an experimental study that shows significant improvements of the tagging process and execution times on a dataset containing triples in a FOAF graph.