Università  degli Studi di Udine

We propose a family of Markov chain-based models for the link analysis of scientific publications. The PageRank-style model and the dummy paper model can be obtained by the suitable
instantiation of its parameters. Since scientific publications can be ordered by the date of publication it is natural to assume a triangular structure for the adjacency matrix of the citation graph. This greatly simplify the updating of the ranking vector if new papers are added to the database. In addition by assuming that the citation graph can be modeled as a fixed degree random sequence graph we can obtain an explicit estimation of the behavior of the entries of the ranking vector.