Bibliographic databases are a prosperous field for data mining research and social network analysis. The representation and visualization of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge regarding how the publication records of authors evolve over time. In this paper we propose a novel methodology to model bibliographical databases as Power Graphs, and mine them in an unsupervised manner, in order to learn basic author types and their properties through clustering. The methodology takes into account the evolution of the co-authorship information, the volume of published papers over time, as well as the impact factors of the venues hosting the respective publications. As a proof of concept of the applicability and scalability of our approach, we present experimental results in the DBLP data.