Versatile weighting strategies for a citation-based research evaluation model

  In this paper, we first give a quick review of the most used numerical indicators for evaluating research, and then we present an integrated  model for ranking scientific publications together with authors and journals. Our model relies on certain adiacency matrices obtained from the relations of citation, authorship and publication. These matrices are first normalized to obtain stochastic matrices and then are combined together by means of weights to  form a suitable irreducible stochastic matrix whose dominant eigenvector provides the ranking. We discuss various strategies for choosing the weights and we show on large  synthetic datasets how the choice of a weighting criteria rather than another can change the behavior of our ranking algorithm.