Title: A Graph Analytics Framework for Ranking Authors, Papers and Venues,
Abstract: A lot of scientific works are published in different areas of science,
technology, engineering and mathematics. It is not easy, even for experts, to
judge the quality of authors, papers and venues (conferences and journals). An
objective measure to assign scores to these entities and to rank them is very
useful. Although, several metrics and indexes have been proposed earlier, they
suffer from various problems. In this paper, we propose a graph-based analytics
framework to assign scores and to rank authors, papers and venues. Our
algorithm considers only the link structures of the underlying graphs. It does
not take into account other aspects, such as the associated texts and the
reputation of these entities. In the limit of large number of iterations, the
solution of the iterative equations gives the unique entity scores. This
framework can be easily extended to other interdependent networks.