Publication Induced Research Analysis (PIRA) - Experiments on Real Data

TL;DR

This paper introduces a novel PageRank-based method for ranking papers and authors in a bipartite graph, demonstrating improved relevance on real bibliographic data from DBLP and CiteseerX.

Contribution

The paper presents a new PageRank-inspired algorithm for research activity evaluation on heterogeneous graphs, showing its effectiveness over existing methods on real datasets.

Findings

Our method yields more pertinent rankings than traditional PageRank applications.

The approach performs consistently well across different real data scenarios.

Results suggest potential for optimizing research impact assessment.

Abstract

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.