  The number of citations is a widely used metric to evaluate the scientific
credit of papers, scientists and journals. However, it does happen that a paper
with fewer citations from prestigious scientists is of higher influence than
papers with more citations. In this paper, we argue that from whom the paper is
being cited is of higher significance than merely the number of received
citations. Accordingly, we propose an interactive model on author-paper
bipartite networks as well as an iterative algorithm to get better rankings for
scientists and their publications. The main advantage of this method is
twofold: (i) it is a parameter-free algorithm; (ii) it considers the
relationship between the prestige of scientists and the quality of their
publications. We conducted real experiments on publications in econophysics,
and applied this method to evaluate the influences of related scientific
journals. The comparisons between the rankings by our method and simple
citation counts suggest that our method is effective to distinguish prestige
from popularity.
