The booming growth of digitally available information has thoroughly increased the popularity of search engine technology over the past years. At the same time, upon interacting with this overwhelming quantity of data, people usually inspect only the very few most relevant items for their task. It is thus very important to utilize high quality ranking measures which efficiently identify these items under the various information retrieval activities we pursue. In this thesis we provide a twofold contribution to the Information Retrieval field. First, we identify those application areas in which a user oriented ranking is missing, though extremely necessary in order to facilitate a qualitative access to relevant resources. Second, for each of these areas we propose appropriate ranking algorithms which exploit their underlying social characteristics, either at the macroscopic, or at the microscopic level. We achieve this by utilizing link analysis techniques, which build on top of the graph based representation of relations between resources in order to rank them or simply to identify social patterns relative to the investigated data set. W
