A Graph Analytics Framework for Ranking Authors, Papers and Venues

TL;DR

This paper introduces a graph-based framework for objectively ranking authors, papers, and venues using link structures, providing a scalable and extendable method without relying on textual or reputation data.

Contribution

It presents a novel graph analytics approach that assigns scores based solely on link structures, improving objectivity and scalability in ranking scientific entities.

Findings

The framework converges to unique scores after sufficient iterations.

It effectively ranks entities without using textual or reputation information.

The method is adaptable to other interdependent networks.

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.