PAV: A novel model for ranking heterogeneous objects in bibliographic information networks

Bibliographic information networks, formed by online bibliographic databases, such as ACM Digital Library and IEEE/IET Electronic Library, contain abundant information about authors, papers, venues (journals/conferences), and have been widely studies in recent years. However, few studies examine the problem of ranking objects in these networks. In this paper, we study this problem and present a novel model, called PAV, for ranking heterogeneous objects, such as authors, papers, and venues. Based on PAV model, we transform the problem of ranking objects into the problem of estimating probability distribution. We propose an efficient algorithm to estimate probability parameters by use of the fact that the PAV model is a regular Markov chain. For evaluating PAV model, we apply it on one real dataset, which was crawled from ACM Digital Library. The experimental results show that the proposed model is effective.