A new challenge that consists in accessing to multiple relevant entities arises from the availability of linked heterogeneous data. In this paper, we address more specifically the problem of accessing to relevant entities, such as publications and authors within a bibliographic network, considering an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network- based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analysed through a language model-based score on the one hand and on the other hand, non-topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using Bibrank algorithm within an information retrieval task. CiteSeerX bibliographic dataset forms the basis for the topical query automatic generation and evaluation. We show that statistically significant improvement over closely related ranking models is achieved.