After a quick review of the most used numerical indicators for evaluating
research, we present an integrated model for ranking scientific publications
together with authors and journals. Our model relies on certain adjacentcy
matrices obtained from the relationship between papers, authors, and
journals. These matrices are first normalized to obtain stochastic matrices
and then are combined together using appropriate weights to form a suitable
irreducible stochastic matrix whose dominant eigenvector provides the desired
ranking. Our main contribution is a in-depth analysis of various strategies
for choosing the weights, showing their probabilistic interpretation and
showing how they affect the outcome of the ranking process. We also prove
that, by solving an inverse eigenvector problem, we can determine a weighting
strategy in which the relative importance of papers, authors, and journals is
chosen by the final user of the ranking algorithm. The impact of the
different weighting strategies is analyzed also by means of extensive
experiments on large synthetic datasets.