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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. [ABSTRACT FROM AUTHOR]

In this paper, we extend the notion of similarity matrix, which has been used to define similarity between nodes of two graphs, to the case of colored graphs, where the coloring is either on the nodes or on the edges of both graphs. The proposed method tries to find the optimal matching between the nodes or edges of both graphs but only performs the comparison when their colors are the same. The proposed cost function nevertheless uses the connectivity between all nodes and edges of both graphs. We then also show how to extend this to the notion of low rank similarity matrix, by defining it as a constrained optimization problem. [ABSTRACT FROM AUTHOR]