Bibliographic documents are basically associated with many entities including authors, venues, affiliations, etc. While bibliographic search engines addressed mainly relevant document ranking according to a query topic, ranking other related relevant bibliographic entities is still challenging. Indeed, document relevance is the primary level that allows inferring the relevance of the other entities regardless of the query topic. In this paper, we propose a novel integrated ranking model, called BibRank, that aims at ranking both document and author entities in bibliographic networks. The underlying algorithm propagates entity scores through the network by means of citation and authorship links. Moreover, we propose to weight these relationships using content-based indicators that estimate the topical relatedness between entities. In particular, we estimate the common similarity between homogeneous entities by analyzing marginal citations. We also compare document and author language models in order to evaluate the level of author's knowledge on the document topic and the document representativeness of author's knowledge. Experiment results on the representative CiteSeerX dataset show that BibRank model outperforms state-of-the-art ranking models with a significant improvement.