  This paper describes the first results obtained by implementing a novel
approach to rank vertices in a heterogeneous graph, based on the PageRank
family of algorithms and applied here to the bipartite graph of papers and
authors as a first evaluation of its relevance on real data samples. With this
approach to evaluate research activities, the ranking of a paper/author depends
on that of the papers/authors citing it/him or her. We compare the results
against existing ranking methods (including methods which simply apply PageRank
to the graph of papers or the graph of authors) through the analysis of simple
scenarios based on a real dataset built from DBLP and CiteseerX. The results
show that in all examined cases the obtained result is most pertinent with our
method which allows to orient our future work to optimizing the execution of
this algorithm.
